Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
We're looking for a founding Product Leader to own the roadmap for Minerva's agentic marketing platform. You're a technically fluent, metrics-driven builder who has shipped AI-native or data-rich products. You can move fluidly between the workflows a marketer runs every day and the consumer graph and data systems that power them.
This is a zero-to-one role. You'll define how our agents turn the consumer graph into measurable outcomes (higher ROAS, faster onboarding, less wasted spend) across the full marketing workflow: data unification, audience generation, campaign creation, activation, measurement and reporting. You'll work directly with the founders and be the connective tissue between our data team, engineering and our customers.
Own the product roadmap for the agentic marketing platform, from the marketer-facing agent experience to the consumer-graph foundation that powers it
Define how our agents (Agentic Data Engineer and Agentic Data Scientist) deliver outcomes across the workflow: unifying first-party data, enriching it with our consumer context, generating and optimizing campaigns and measuring performance
Shape the consumer graph as the platform's foundation (coverage, freshness and identity resolution quality at 270M+ profile scale) in direct service of campaign performance
Own the activation surface: how customers push campaigns to the ad platforms, marketing automation and CRMs they already use, plus the Headless Platform (API/MCP) for product teams embedding our intelligence
Partner with engineering and data science to close the loop between usage signals, customer outcomes and the roadmap
Run a tight feedback loop with revenue and forward-deployed teams to translate field insight into product decisions
Define success metrics that matter to commercial leaders: ROAS lift, time-to-value, campaign performance, activation and expansion
5+ years of product experience, with meaningful time on AI-native, data-rich or platform products
Fluency across both sides of the product: the commercial workflows (campaigns, channels, attribution) and the data foundation (schemas, match rates, identity resolution)
A track record shipping products that serve both technical and commercial buyers. You can earn the trust of a data team and a CMO in the same week
Comfort translating customer outcomes and usage data into clear product priorities and roadmap decisions
Experience working in person at a small, fast-moving startup where everyone builds
Strong written communication: you can write a tight PRD, a clear spec and a compelling customer-facing narrative
Experience building AI-native or agentic products: LLM-powered workflows, copilots or orchestration systems
Background in martech, adtech, consumer data or identity: real exposure to the marketing stack marketers actually use
Familiarity with the GTM and activation stack: ad platforms (Meta, Google), marketing automation, Salesforce, HubSpot, Clay, Snowflake, CDPs
Experience building a PLG or API trial motion alongside a sales-assisted enterprise funnel
Understanding of consumer data compliance: CCPA, CPRA, data broker regulations, opt-out infrastructure
Founder-adjacent experience or early PM hire at a data-native company
$234,000–$262,000 base salary, commensurate with experience. Meaningful equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nWe're looking for a founding Product Leader to own the roadmap for Minerva's agentic marketing platform. You're a technically fluent, metrics-driven builder who has shipped AI-native or data-rich products. You can move fluidly between the workflows a marketer runs every day and the consumer graph and data systems that power them.\n\nThis is a zero-to-one role. You'll define how our agents turn the consumer graph into measurable outcomes (higher ROAS, faster onboarding, less wasted spend) across the full marketing workflow: data unification, audience generation, campaign creation, activation, measurement and reporting. You'll work directly with the founders and be the connective tissue between our data team, engineering and our customers.\n\n\nWHAT YOU'LL DO\n\n - Own the product roadmap for the agentic marketing platform, from the marketer-facing agent experience to the consumer-graph foundation that powers it\n\n - Define how our agents (Agentic Data Engineer and Agentic Data Scientist) deliver outcomes across the workflow: unifying first-party data, enriching it with our consumer context, generating and optimizing campaigns and measuring performance\n\n - Shape the consumer graph as the platform's foundation (coverage, freshness and identity resolution quality at 270M+ profile scale) in direct service of campaign performance\n\n - Own the activation surface: how customers push campaigns to the ad platforms, marketing automation and CRMs they already use, plus the Headless Platform (API/MCP) for product teams embedding our intelligence\n\n - Partner with engineering and data science to close the loop between usage signals, customer outcomes and the roadmap\n\n - Run a tight feedback loop with revenue and forward-deployed teams to translate field insight into product decisions\n\n - Define success metrics that matter to commercial leaders: ROAS lift, time-to-value, campaign performance, activation and expansion\n\n\nWHAT YOU HAVE\n\n - 5+ years of product experience, with meaningful time on AI-native, data-rich or platform products\n\n - Fluency across both sides of the product: the commercial workflows (campaigns, channels, attribution) and the data foundation (schemas, match rates, identity resolution)\n\n - A track record shipping products that serve both technical and commercial buyers. You can earn the trust of a data team and a CMO in the same week\n\n - Comfort translating customer outcomes and usage data into clear product priorities and roadmap decisions\n\n - Experience working in person at a small, fast-moving startup where everyone builds\n\n - Strong written communication: you can write a tight PRD, a clear spec and a compelling customer-facing narrative\n\n\nNICE TO HAVES\n\n - Experience building AI-native or agentic products: LLM-powered workflows, copilots or orchestration systems\n\n - Background in martech, adtech, consumer data or identity: real exposure to the marketing stack marketers actually use\n\n - Familiarity with the GTM and activation stack: ad platforms (Meta, Google), marketing automation, Salesforce, HubSpot, Clay, Snowflake, CDPs\n\n - Experience building a PLG or API trial motion alongside a sales-assisted enterprise funnel\n\n - Understanding of consumer data compliance: CCPA, CPRA, data broker regulations, opt-out infrastructure\n\n - Founder-adjacent experience or early PM hire at a data-native company\n\n\nCOMPENSATION\n\n$234,000–$262,000 base salary, commensurate with experience. Meaningful equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"20bba7d5-f862-42db-afc6-d01d56f6bf1d","title":"Senior Enterprise Account Executive","department":"Go-to-Market","team":"Sales","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-06-07T16:07:15.479+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/20bba7d5-f862-42db-afc6-d01d56f6bf1d","applyUrl":"https://jobs.ashbyhq.com/minerva/20bba7d5-f862-42db-afc6-d01d56f6bf1d/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Inbound pipeline driven by platform demos, API trial sign-ups and word of mouth from data-forward GTM teams
Core buyers are commercial leaders at consumer brands: CMOs, CROs and Heads of Sales, plus product leaders embedding our intelligence through the Headless API
Deal cycles are typically 2–4 weeks; platform subscription plus usage-based pricing with strong expansion potential
Significant upsell opportunity: customers start with one workflow (paid media optimization, direct mail or lead triage) and expand across the full platform
We win on outcomes (higher ROAS, faster time-to-value, better campaign performance) backed by the match rate and depth of our consumer graph
You'll be one of Minerva's first dedicated AEs, owning our inbound pipeline and working directly with the founders to close customers. This is a high-autonomy, high-impact role at an early stage company where your output directly shapes revenue and our understanding of the market.
Own the full sales cycle from inbound demo to closed deal
Run discovery calls that uncover the prospect's data stack, use cases and key success metrics
Deliver compelling demos of Minerva's agentic platform, from the marketer-facing workflows (data unification, campaign creation, measurement) to the Headless API for product teams
Work cross-functionally with engineering and data to handle custom scoping, match rate tests and POC requests
Maintain accurate pipeline in CRM and provide forecasting to founders
Feed market signal back to the product and data team: your customers will define our roadmap
5+ years of SaaS/AI sales experience, ideally at a data, API or AI-native company
Experience closing ACVs of $50K–$250K
Track record of quota attainment (President's Club, Top 10%, etc.)
Comfort selling to a mix of commercial buyers (CMOs, Heads of Growth) and technical buyers (engineers, data scientists)
Located in or willing to relocate to New York City
Background selling data, API or martech products into GTM teams at consumer brands
Familiarity with martech, adtech, consumer data or identity
Familiar with product-led growth motions, using free API trials or credits as a primary pipeline channel
Experience in a GTM team of fewer than 10 people
Have sold into Marketing, Growth or RevOps teams at B2C or B2B2C companies
$176,000–$197,000 base salary, with on-target earnings of $352,000–$394,000 at a 50/50 base/variable split. Uncapped commission. Equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nGTM AT MINERVA\n\n - Inbound pipeline driven by platform demos, API trial sign-ups and word of mouth from data-forward GTM teams\n\n - Core buyers are commercial leaders at consumer brands: CMOs, CROs and Heads of Sales, plus product leaders embedding our intelligence through the Headless API\n\n - Deal cycles are typically 2–4 weeks; platform subscription plus usage-based pricing with strong expansion potential\n\n - Significant upsell opportunity: customers start with one workflow (paid media optimization, direct mail or lead triage) and expand across the full platform\n\n - We win on outcomes (higher ROAS, faster time-to-value, better campaign performance) backed by the match rate and depth of our consumer graph\n\n\nTHE ROLE\n\nYou'll be one of Minerva's first dedicated AEs, owning our inbound pipeline and working directly with the founders to close customers. This is a high-autonomy, high-impact role at an early stage company where your output directly shapes revenue and our understanding of the market.\n\n\nWHAT YOU'LL DO\n\n - Own the full sales cycle from inbound demo to closed deal\n\n - Run discovery calls that uncover the prospect's data stack, use cases and key success metrics\n\n - Deliver compelling demos of Minerva's agentic platform, from the marketer-facing workflows (data unification, campaign creation, measurement) to the Headless API for product teams\n\n - Work cross-functionally with engineering and data to handle custom scoping, match rate tests and POC requests\n\n - Maintain accurate pipeline in CRM and provide forecasting to founders\n\n - Feed market signal back to the product and data team: your customers will define our roadmap\n\n\nWHAT YOU HAVE\n\n - 5+ years of SaaS/AI sales experience, ideally at a data, API or AI-native company\n\n - Experience closing ACVs of $50K–$250K\n\n - Track record of quota attainment (President's Club, Top 10%, etc.)\n\n - Comfort selling to a mix of commercial buyers (CMOs, Heads of Growth) and technical buyers (engineers, data scientists)\n\n - Located in or willing to relocate to New York City\n\n\nNICE TO HAVES\n\n - Background selling data, API or martech products into GTM teams at consumer brands\n\n - Familiarity with martech, adtech, consumer data or identity\n\n - Familiar with product-led growth motions, using free API trials or credits as a primary pipeline channel\n\n - Experience in a GTM team of fewer than 10 people\n\n - Have sold into Marketing, Growth or RevOps teams at B2C or B2B2C companies\n\n\nCOMPENSATION\n\n$176,000–$197,000 base salary, with on-target earnings of $352,000–$394,000 at a 50/50 base/variable split. Uncapped commission. Equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"03c28201-0212-4e33-87b4-3b343ee924ec","title":"General Application","department":"General","team":"General","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-06-07T17:21:31.792+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/03c28201-0212-4e33-87b4-3b343ee924ec","applyUrl":"https://jobs.ashbyhq.com/minerva/03c28201-0212-4e33-87b4-3b343ee924ec/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
We can't predict every role we need to fill but we certainly need more hands! Describe yourself and your ideal role and we'll let you know if we see a fit!
$150,000–$250,000 base salary, depending on the role and level. Meaningful equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nWe can't predict every role we need to fill but we certainly need more hands! Describe yourself and your ideal role and we'll let you know if we see a fit!\n\n\nCOMPENSATION\n\n$150,000–$250,000 base salary, depending on the role and level. Meaningful equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"632fa6ea-fc58-4474-b361-73b9fbefc598","title":"Staff Data Engineer","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-06-09T18:59:16.719+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/632fa6ea-fc58-4474-b361-73b9fbefc598","applyUrl":"https://jobs.ashbyhq.com/minerva/632fa6ea-fc58-4474-b361-73b9fbefc598/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
We run a multi-tenant transformation platform that unifies a portfolio of customer brands into a single golden data model across Shopify, HubSpot, Klaviyo, Google/Meta Ads and more.
You'll be the technical owner of this part of our broader data platform: the person who designs for the regime we're growing into rather than the one we're in. The defining question of the role is how to make the cost of the next tenant (and the next data source, the next entity in our ontology, the next golden model) flat instead of linear, while correctness, isolation and freshness hold as the system fans out.
Increasingly, that platform isn't operated only by people. In partnership with OpenAI, our Agentic Data Engineer (ADE) already standardizes bespoke client data into golden records, and downstream agents like our Agentic Data Scientist (ADS) build models on top of it. You own the substrate they stand on (the contracts, the golden model, the access layer) and the systems that expose our data to internal and external AI agents (MCP, vector search) as a first-class channel. We believe agentic access to our data is just as important as the traditional API and ETL paths, if not more, and this role builds for that.
This is a build-from-strength role. We expect data cleaning, modeling and warehouse fluency to be second nature so your thinking is free for architecture and large-scale initiatives, especially given how much leverage modern AI coding tools give a strong engineer.
Design the tenant-onboarding model so adding the Nth tenant is a config-and-metadata operation, not bespoke engineering, driving source routing, transformation, model membership and per-tenant view generation from a single source of truth rather than hand-assembly
Own correctness guarantees across a wide fan-out of tenants and models: the class of problem where coverage gaps, contract drift and partial rollouts can hide unless the system makes them structurally impossible. Build the contracts, audits and CI gates that turn \"is every tenant fully represented?\" into something the build answers, not a person
Own the org-identity and access layer (tenant provisioning, source-to-tenant routing, multi-tenant isolation and RBAC) so it holds at hundreds of tenants without per-tenant special cases
Build the platform machinery that lets our data ontology grow (new entities, attributes and behaviors) without breaking everything downstream. Own how the ontology is represented, versioned and propagated: schema evolution, contract migration and backward compatibility across hundreds of tenants and the models built on them
Partner with the field as our FDEs and customers surface the new concepts a customer needs, making adding them to the canonical model a safe, repeatable operation rather than a risky one-off
Build the systems that expose Minerva's data product to internal and external AI agents (MCP servers, vector search, semantic and metrics layers) so agents can query, reason over and act on our data safely. Treat agentic access as a first-class delivery channel alongside API, batch ETL and CRM integrations
Own the substrate our agentic workflows depend on: the golden-record contracts ADE produces and ADS consumes. Build the guardrails, validation and observability that let agent-generated transformations be trusted in production at scale. You build the reliability layer under the agents
Lead the evaluation and migration of our orchestration and transformation stack as throughput, concurrency and latency demands outgrow what today's tooling is built for, incrementally, without halting the business running on top of it
Architect the path to real-time: keeping the analytical warehouse in sync with the Postgres/Elasticsearch product backends and standing up the infrastructure for the CDP (streaming audiences, identity resolution, reverse-ETL to ad platforms and CRMs)
Define how pipelines are built, tested, deployed and governed as the team and tenant count grow: isolation, privacy/opt-out enforcement and data-quality SLAs as first-class platform properties
5+ years in data/software engineering in a data-heavy context, with real ownership of a production data platform at scale (many sources, many consumers)
Expert Python and SQL, fluent enough that ingestion and modeling are reflexes, not projects
Deep experience with transformation and orchestration tooling (SQLMesh, dbt, Airflow or equivalent) and a point of view on where they break and what comes next
Strong grasp of analytical warehouses (Snowflake/Redshift/BigQuery) and how they interplay with transactional stores (Postgres/MySQL)
A track record of multi-tenant design: isolation, governance and secure access patterns as tenant count climbs
Demonstrated judgment on the stability vs. speed of development tradeoff: you've kept a platform reliable for its users while still letting a team ship fast on top of it, and you can articulate how you decided where each belonged
Experience evolving a schema, data model or ontology in production (versioning, migration and backward compatibility) without breaking downstream consumers
Interest in (or experience) exposing data to AI agents (MCP, vector search, semantic layers, retrieval) and a conviction that agentic access is a first-class way to deliver a data product
Comfort owning ambiguous, cross-cutting initiatives end-to-end in a lean, fast-changing environment. Willing to work onsite in NYC (relocation provided)
Lakehouse or Spark experience and comfort handling very large datasets
Built or operated a CDP, identity graph or reverse-ETL/activation system
Strong communication skills, preferably has interfaced with enterprise customers before
Data lineage and reactive-propagation systems; streaming (Kafka/Flink/Snowpipe)
Backend or ML/AI-eng exposure; feature engineering and training infra are adjacent to this role
Heavy and efficient use of AI coding tools (Claude Code, Cursor, etc.) as a force multiplier
Built infrastructure for agentic or LLM workflows on top of data (OpenAI Agents SDK, MCP servers, vector DBs, retrieval and eval harnesses)
Prior early-stage startup experience
Counterpart roles: you own the technical side of evolving the ontology and platform. Our Backend Engineer, Data owns the product side: what gets precomputed and how it reaches the app, built on the semantic views you publish. Our forward deployed engineers sit with the customer, expand the ontology from the field and feed you the patterns worth productizing.
$237,000–$265,000 base salary, commensurate with experience. Meaningful equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nWe run a multi-tenant transformation platform that unifies a portfolio of customer brands into a single golden data model across Shopify, HubSpot, Klaviyo, Google/Meta Ads and more.\n\nYou'll be the technical owner of this part of our broader data platform: the person who designs for the regime we're growing into rather than the one we're in. The defining question of the role is how to make the cost of the next tenant (and the next data source, the next entity in our ontology, the next golden model) flat instead of linear, while correctness, isolation and freshness hold as the system fans out.\n\nIncreasingly, that platform isn't operated only by people. In partnership with OpenAI, our Agentic Data Engineer (ADE) already standardizes bespoke client data into golden records, and downstream agents like our Agentic Data Scientist (ADS) build models on top of it. You own the substrate they stand on (the contracts, the golden model, the access layer) and the systems that expose our data to internal and external AI agents (MCP, vector search) as a first-class channel. We believe agentic access to our data is just as important as the traditional API and ETL paths, if not more, and this role builds for that.\n\nThis is a build-from-strength role. We expect data cleaning, modeling and warehouse fluency to be second nature so your thinking is free for architecture and large-scale initiatives, especially given how much leverage modern AI coding tools give a strong engineer.\n\n\nWHAT YOU'LL DO\n\n - Design the tenant-onboarding model so adding the Nth tenant is a config-and-metadata operation, not bespoke engineering, driving source routing, transformation, model membership and per-tenant view generation from a single source of truth rather than hand-assembly\n\n - Own correctness guarantees across a wide fan-out of tenants and models: the class of problem where coverage gaps, contract drift and partial rollouts can hide unless the system makes them structurally impossible. Build the contracts, audits and CI gates that turn \"is every tenant fully represented?\" into something the build answers, not a person\n\n - Own the org-identity and access layer (tenant provisioning, source-to-tenant routing, multi-tenant isolation and RBAC) so it holds at hundreds of tenants without per-tenant special cases\n\n - Build the platform machinery that lets our data ontology grow (new entities, attributes and behaviors) without breaking everything downstream. Own how the ontology is represented, versioned and propagated: schema evolution, contract migration and backward compatibility across hundreds of tenants and the models built on them\n\n - Partner with the field as our FDEs and customers surface the new concepts a customer needs, making adding them to the canonical model a safe, repeatable operation rather than a risky one-off\n\n - Build the systems that expose Minerva's data product to internal and external AI agents (MCP servers, vector search, semantic and metrics layers) so agents can query, reason over and act on our data safely. Treat agentic access as a first-class delivery channel alongside API, batch ETL and CRM integrations\n\n - Own the substrate our agentic workflows depend on: the golden-record contracts ADE produces and ADS consumes. Build the guardrails, validation and observability that let agent-generated transformations be trusted in production at scale. You build the reliability layer under the agents\n\n - Lead the evaluation and migration of our orchestration and transformation stack as throughput, concurrency and latency demands outgrow what today's tooling is built for, incrementally, without halting the business running on top of it\n\n - Architect the path to real-time: keeping the analytical warehouse in sync with the Postgres/Elasticsearch product backends and standing up the infrastructure for the CDP (streaming audiences, identity resolution, reverse-ETL to ad platforms and CRMs)\n\n - Define how pipelines are built, tested, deployed and governed as the team and tenant count grow: isolation, privacy/opt-out enforcement and data-quality SLAs as first-class platform properties\n\n\nWHAT YOU HAVE\n\n - 5+ years in data/software engineering in a data-heavy context, with real ownership of a production data platform at scale (many sources, many consumers)\n\n - Expert Python and SQL, fluent enough that ingestion and modeling are reflexes, not projects\n\n - Deep experience with transformation and orchestration tooling (SQLMesh, dbt, Airflow or equivalent) and a point of view on where they break and what comes next\n\n - Strong grasp of analytical warehouses (Snowflake/Redshift/BigQuery) and how they interplay with transactional stores (Postgres/MySQL)\n\n - A track record of multi-tenant design: isolation, governance and secure access patterns as tenant count climbs\n\n - Demonstrated judgment on the stability vs. speed of development tradeoff: you've kept a platform reliable for its users while still letting a team ship fast on top of it, and you can articulate how you decided where each belonged\n\n - Experience evolving a schema, data model or ontology in production (versioning, migration and backward compatibility) without breaking downstream consumers\n\n - Interest in (or experience) exposing data to AI agents (MCP, vector search, semantic layers, retrieval) and a conviction that agentic access is a first-class way to deliver a data product\n\n - Comfort owning ambiguous, cross-cutting initiatives end-to-end in a lean, fast-changing environment. Willing to work onsite in NYC (relocation provided)\n\n\nNICE TO HAVES\n\n - Lakehouse or Spark experience and comfort handling very large datasets\n\n - Built or operated a CDP, identity graph or reverse-ETL/activation system\n\n - Strong communication skills, preferably has interfaced with enterprise customers before\n\n - Data lineage and reactive-propagation systems; streaming (Kafka/Flink/Snowpipe)\n\n - Backend or ML/AI-eng exposure; feature engineering and training infra are adjacent to this role\n\n - Heavy and efficient use of AI coding tools (Claude Code, Cursor, etc.) as a force multiplier\n\n - Built infrastructure for agentic or LLM workflows on top of data (OpenAI Agents SDK, MCP servers, vector DBs, retrieval and eval harnesses)\n\n - Prior early-stage startup experience\n\nCounterpart roles: you own the technical side of evolving the ontology and platform. Our Backend Engineer, Data owns the product side: what gets precomputed and how it reaches the app, built on the semantic views you publish. Our forward deployed engineers sit with the customer, expand the ontology from the field and feed you the patterns worth productizing.\n\n\nCOMPENSATION\n\n$237,000–$265,000 base salary, commensurate with experience. Meaningful equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"d5f0d1de-5dcf-4e16-893f-2eda40c55b5e","title":"Data Scientist","department":"Research & Development","team":"Data Science & Analytics","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-06-24T14:35:11.153+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/d5f0d1de-5dcf-4e16-893f-2eda40c55b5e","applyUrl":"https://jobs.ashbyhq.com/minerva/d5f0d1de-5dcf-4e16-893f-2eda40c55b5e/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
As a Data Scientist at Minerva, you build the models and features that power our consumer graph and the agents that run on top of it. You sit at the intersection of heavy data engineering and applied modeling: you architect feature engineering pipelines that are computed over terabytes of data, train and sharpen the models that drive targeting and prediction, and ensure the outputs are robust enough to be consumed autonomously by our Minerva Agents and our world-class modeled attributes (i.e. income / wealth).
This is a role that will be deploying constantly to production. The models you build are not handed off to be deployed by someone else, you own the path from raw data to a feature or model that an agent can call reliably at scale. As we grow, your work becomes the foundation other systems are built on.
Create new features for models and agents, expanding the predictive surface area of our consumer data lake and building the pipelines that turn raw signal into trusted attributes.
Improve existing models through rigorous feature engineering, including our income/wealth, home buyer, and home seller models.
Play a pivotal role in the buildout of our world-class data lake, shaping how terabytes of consumer data are stored, transformed, and made queryable for both humans and agents.
Build feature engineering pipelines that run efficiently at terabyte scale, with the data engineering rigor to make them reliable in production. This is a 70/30 split DS/DE role.
Ensure model and feature outputs are reliable enough to be consumed agentically, writing the validations and guardrails that let our agents act on your work without a human in the loop.
Dagster for all things orchestration
dbt-core within Dagster as the primary data transformation surface
Spark, Iceberg, Trino, AWS Glue for Lakehouse workloads
Modal for ML eng
Frontier + OSS models & agent SDKs. We are heavy users of OpenAI/Anthropic batch APIs
2-4+ years working as a data scientist, applied machine learning focused data engineer or software engineer in a data-heavy context. Simply put, you live and breathe data.
Highly proficient at Python and SQL.
You are driven by first-principles thinking and are a go-getter. You reason about what datasets and features are necessary to solve a modeling problem, and are scrappy and clever enough to bring that to life.
Strong intuition for data engineering principles, especially around data cleaning/ingestion and data modeling. We prefer these core skills to be second-nature, freeing up thinking for architecting and executing large-scale data initiatives, especially given the advancement of AI coding tools.
Strong engineering background. You are comfortable deploying complicated production pipelines and working within larger production systems, not just in sandboxed or research environments.
Willingness to work in office in NYC (we provide a relocation package).
Flexibility and openness to wearing several hats. We are lean and things are always changing.
Eagerness to learn and grow with the company and your coworkers.
Experience building and training predictive models (e.g. lead scoring, LTV, propensity, lookalike modeling).
Experience with orchestration tools like Dagster, Airflow, Prefect and SQL transformation tools like dbt, SQLMesh.
Experience with both transactional databases (e.g. Postgres, MySQL) and analytical databases (e.g. Snowflake, Redshift), with a bias toward the latter.
Familiarity with a cloud resource provider (e.g. AWS, GCP).
Familiarity with backend and ML/AI engineering.
Experience with AI coding tools (e.g. Cursor, Claude Code, OpenCode) as a force multiplier.
Prior work at an early-stage startup.
You don't need to tick every box. If you're strong on the engineering side and hungry to build models that matter, we want to hear from you.
$160,000–$230,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nAs a Data Scientist at Minerva, you build the models and features that power our consumer graph and the agents that run on top of it. You sit at the intersection of heavy data engineering and applied modeling: you architect feature engineering pipelines that are computed over terabytes of data, train and sharpen the models that drive targeting and prediction, and ensure the outputs are robust enough to be consumed autonomously by our Minerva Agents and our world-class modeled attributes (i.e. income / wealth).\n\nThis is a role that will be deploying constantly to production. The models you build are not handed off to be deployed by someone else, you own the path from raw data to a feature or model that an agent can call reliably at scale. As we grow, your work becomes the foundation other systems are built on.\n\n\nWHAT YOU'LL DO\n\n - Create new features for models and agents, expanding the predictive surface area of our consumer data lake and building the pipelines that turn raw signal into trusted attributes.\n\n - Improve existing models through rigorous feature engineering, including our income/wealth, home buyer, and home seller models.\n\n - Play a pivotal role in the buildout of our world-class data lake, shaping how terabytes of consumer data are stored, transformed, and made queryable for both humans and agents.\n\n - Build feature engineering pipelines that run efficiently at terabyte scale, with the data engineering rigor to make them reliable in production. This is a 70/30 split DS/DE role.\n\n - Ensure model and feature outputs are reliable enough to be consumed agentically, writing the validations and guardrails that let our agents act on your work without a human in the loop.\n\n\nOUR DATA STACK\n\n - Dagster for all things orchestration\n\n - dbt-core within Dagster as the primary data transformation surface\n\n - Spark, Iceberg, Trino, AWS Glue for Lakehouse workloads\n\n - Modal for ML eng\n\n - Frontier + OSS models & agent SDKs. We are heavy users of OpenAI/Anthropic batch APIs\n\n\nQUALIFICATIONS\n\n - 2-4+ years working as a data scientist, applied machine learning focused data engineer or software engineer in a data-heavy context. Simply put, you live and breathe data.\n\n - Highly proficient at Python and SQL.\n\n - You are driven by first-principles thinking and are a go-getter. You reason about what datasets and features are necessary to solve a modeling problem, and are scrappy and clever enough to bring that to life.\n\n - Strong intuition for data engineering principles, especially around data cleaning/ingestion and data modeling. We prefer these core skills to be second-nature, freeing up thinking for architecting and executing large-scale data initiatives, especially given the advancement of AI coding tools.\n\n - Strong engineering background. You are comfortable deploying complicated production pipelines and working within larger production systems, not just in sandboxed or research environments.\n\n - Willingness to work in office in NYC (we provide a relocation package).\n\n - Flexibility and openness to wearing several hats. We are lean and things are always changing.\n\n - Eagerness to learn and grow with the company and your coworkers.\n\n\nPREFERRED\n\n - Experience building and training predictive models (e.g. lead scoring, LTV, propensity, lookalike modeling).\n\n - Experience with orchestration tools like Dagster, Airflow, Prefect and SQL transformation tools like dbt, SQLMesh.\n\n - Experience with both transactional databases (e.g. Postgres, MySQL) and analytical databases (e.g. Snowflake, Redshift), with a bias toward the latter.\n\n - Familiarity with a cloud resource provider (e.g. AWS, GCP).\n\n - Familiarity with backend and ML/AI engineering.\n\n - Experience with AI coding tools (e.g. Cursor, Claude Code, OpenCode) as a force multiplier.\n\n - Prior work at an early-stage startup.\n\nYou don't need to tick every box. If you're strong on the engineering side and hungry to build models that matter, we want to hear from you.\n\n\nCOMPENSATION\n\n$160,000–$230,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"5c0c0f22-6908-4780-9c04-3d135b8aedb9","title":"Forward Deployed Engineer","department":"Solutions","team":"Solutions","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-27T15:47:14.585+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/5c0c0f22-6908-4780-9c04-3d135b8aedb9","applyUrl":"https://jobs.ashbyhq.com/minerva/5c0c0f22-6908-4780-9c04-3d135b8aedb9/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Every enterprise account moves from a data test to a pilot to production. The technical win usually decides the deal.
Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.
Our solutions engineers get standard accounts live on our product. Some accounts need more than the product does today. A pro sports team with ten years of ticketing data in three systems. A brand that needs a model we haven't built yet. First-party data that needs real work before anyone can trust it.
Those accounts get you. You're a data scientist who ships production pipelines, or a data engineer who builds models, embedded with our most complex customers. You build the custom onboarding, pipelines and models that make Minerva work in their world, then bring what you learned home so the next customer gets it out of the box.
You work closely with our CTO and data science team, alongside each account's AE, forward deployed marketer and solutions engineer.
Get the data ready. Build custom ingestion and pipelines for accounts whose data doesn't fit standard onboarding, and make the call on whether the data is right before anyone builds on it.
Build custom models. Propensity, lookalike and scoring models trained on a customer's first-party data and our consumer graph, when the standard models aren't enough.
Run scoring in production for your accounts, and own its accuracy.
Build the net-new. When an expansion needs something we don't have, scope it with the forward deployed marketer and build it.
Make the one-offs standard. Turn custom pipelines and models into patterns our data platform team can productize.
Be the technical voice in the room when a customer's data team wants to know how it actually works.
4+ years as a data scientist or data engineer shipping to production in a data-heavy setting: models that ran on real data, pipelines other people depended on.
Strong Python and SQL, experience with dbt and an orchestrator like Dagster or Airflow, and a cloud warehouse (Snowflake, BigQuery or Databricks).
You've built predictive models such as propensity, LTV, lookalike or lead scoring, and you know how to tell a customer when their data won't support one.
Messy, multi-source data doesn't scare you: identity resolution, entity matching, consumer or behavioral data.
You've worked directly with customers or non-technical stakeholders, and they asked for you again.
You're in New York City or willing to relocate, and comfortable traveling to customers.
Consumer data, identity graphs, adtech or martech.
Experiment design: holdouts and lift measurement.
Forward deployed, solutions or consulting experience at a data company.
Sports, financial services or travel, where our enterprise motion is strongest.
Standard onboarding. Our solutions engineers get standard accounts live on our product. You come in when an account needs something built, before or after the deal closes.
A research role. Everything you build runs for a paying customer.
First 90 days: you've taken one complex account from raw data to production scoring. Your data-readiness call is written down, and the customer's data team is on your side.
First 6 months: time-to-data-ready on complex accounts has a number, and it's falling. At least one custom pipeline or model has become a standard pattern.
The numbers you own: time-to-data-ready and data quality on your accounts.
$215,000–$241,000 base salary, depending on experience, plus a variable bonus targeted at 10% of base. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Walk us through one model or pipeline you built on someone else's messy data: what the data looked like, what you built, what broke and how it held up in production. A few paragraphs.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nHOW WE DEPLOY\n\n - Every enterprise account moves from a data test to a pilot to production. The technical win usually decides the deal.\n\n - Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.\n\n\nTHE ROLE\n\nOur solutions engineers get standard accounts live on our product. Some accounts need more than the product does today. A pro sports team with ten years of ticketing data in three systems. A brand that needs a model we haven't built yet. First-party data that needs real work before anyone can trust it.\n\nThose accounts get you. You're a data scientist who ships production pipelines, or a data engineer who builds models, embedded with our most complex customers. You build the custom onboarding, pipelines and models that make Minerva work in their world, then bring what you learned home so the next customer gets it out of the box.\n\nYou work closely with our CTO and data science team, alongside each account's AE, forward deployed marketer and solutions engineer.\n\n\nWHAT YOU'LL DO\n\n - Get the data ready. Build custom ingestion and pipelines for accounts whose data doesn't fit standard onboarding, and make the call on whether the data is right before anyone builds on it.\n\n - Build custom models. Propensity, lookalike and scoring models trained on a customer's first-party data and our consumer graph, when the standard models aren't enough.\n\n - Run scoring in production for your accounts, and own its accuracy.\n\n - Build the net-new. When an expansion needs something we don't have, scope it with the forward deployed marketer and build it.\n\n - Make the one-offs standard. Turn custom pipelines and models into patterns our data platform team can productize.\n\n - Be the technical voice in the room when a customer's data team wants to know how it actually works.\n\n\nWHAT YOU HAVE\n\n - 4+ years as a data scientist or data engineer shipping to production in a data-heavy setting: models that ran on real data, pipelines other people depended on.\n\n - Strong Python and SQL, experience with dbt and an orchestrator like Dagster or Airflow, and a cloud warehouse (Snowflake, BigQuery or Databricks).\n\n - You've built predictive models such as propensity, LTV, lookalike or lead scoring, and you know how to tell a customer when their data won't support one.\n\n - Messy, multi-source data doesn't scare you: identity resolution, entity matching, consumer or behavioral data.\n\n - You've worked directly with customers or non-technical stakeholders, and they asked for you again.\n\n - You're in New York City or willing to relocate, and comfortable traveling to customers.\n\n\nNICE TO HAVES\n\n - Consumer data, identity graphs, adtech or martech.\n\n - Experiment design: holdouts and lift measurement.\n\n - Forward deployed, solutions or consulting experience at a data company.\n\n - Sports, financial services or travel, where our enterprise motion is strongest.\n\n\nWHAT THIS ISN'T\n\n - Standard onboarding. Our solutions engineers get standard accounts live on our product. You come in when an account needs something built, before or after the deal closes.\n\n - A research role. Everything you build runs for a paying customer.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: you've taken one complex account from raw data to production scoring. Your data-readiness call is written down, and the customer's data team is on your side.\n\n - First 6 months: time-to-data-ready on complex accounts has a number, and it's falling. At least one custom pipeline or model has become a standard pattern.\n\n - The numbers you own: time-to-data-ready and data quality on your accounts.\n\n\nCOMPENSATION\n\n$215,000–$241,000 base salary, depending on experience, plus a variable bonus targeted at 10% of base. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Walk us through one model or pipeline you built on someone else's messy data: what the data looked like, what you built, what broke and how it held up in production. A few paragraphs.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"dbfa0d14-3fdb-432d-bc03-c0364e313a39","title":"Data Engineer, Data Products","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-08-06T22:59:28.464+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/dbfa0d14-3fdb-432d-bc03-c0364e313a39","applyUrl":"https://jobs.ashbyhq.com/minerva/dbfa0d14-3fdb-432d-bc03-c0364e313a39/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Minerva's data is not just infrastructure beneath our product; it is also one of our products. We are looking for a Data Engineer who can take ownership of complex consumer-data domains, develop a deep understanding of how their datasets relate and turn messy raw signals into trusted attributes and production data products. This role & Minerva are quite unique in the sense that GTM can immediately start selling your work output and generate enterprise-grade revenue in a matter of weeks.
You will spend most of your time at the transformation and derivation layer. You might become Minerva's internal expert on an identity graph, property and professional data, or a new source of consumer intent: learning the domain deeply enough to identify what is useful, what is misleading and what we should build next. Your job is not simply to make data available for someone downstream. You will use it to solve ambiguous problems and move an important part of our data product forward.
This is an end-to-end role. You will investigate novel datasets, source data when the answer is not already available, design domain models and derived attributes, and productionize your work so it can be consumed reliably by Minerva's applications, agents, APIs and ML models. Our data platform engineers build the infra that makes this work scalable; you must be comfortable operating within that infra and building your own ingestion and transformation pipelines without creating a bottleneck for the platform team.
The best fit can come from several backgrounds: a product-oriented data engineer, an applied data scientist who has data engineering skills, or a software engineer who has spent years solving difficult data problems. The common thread is first-principles reasoning, strong engineering fundamentals and a desire to own the answer from raw data through production. We test rigorously for data problem solving skills in our interview process.
Own one or more complex consumer-data domains end-to-end, becoming the person responsible for both understanding the data and advancing the products built from it.
Investigate large, messy and unfamiliar datasets. Establish their grain, keys, relationships, coverage, failure modes and fitness for different product use cases.
Design and build durable domain models, derived attributes and entity relationships that can power Minerva's applications, AI agents, APIs, customer deliveries and predictive models.
Build and operate the ingestion and transformation pipelines required to bring your work into production, including validation, observability, backfills and recovery. Productionization is key.
Go on data quests: identify and evaluate new sources, determine how they can improve our consumer graph and find clever ways to extract signal from imperfect inputs. We often have budget for purchasing new data when there is clear ROI.
Make data outputs trustworthy enough to be consumed autonomously. Define quality checks, provenance and guardrails that distinguish reliable signal from convenient but misleading data.
Partner with data scientists, platform engineers, product engineers and customer-facing teams to turn open-ended business or product questions into scalable data products.
Use LLMs, embeddings and modern AI development tools where they materially improve data standardization, classification, enrichment or engineering velocity.
Find new ways to create and protect business value through Minerva's proprietary data asset, from improving existing products to opening entirely new revenue opportunities.
Dagster for orchestration
dbt-core within Dagster as a primary data-transformation surface
Snowflake for analytical workloads
Spark, Iceberg, Trino and AWS Glue for lakehouse workloads
Postgres, Elasticsearch and other product-facing systems
Frontier and open-source models, agent SDKs and batch APIs from OpenAI and Anthropic
2-5+ years working as a data engineer, software engineer or applied data scientist in a data-heavy context. Your prior title matters less than evidence that you live and breathe data.
Highly proficient in Python and SQL.
Driven by first-principles thinking. You can take an ambiguous data problem, determine what must be true, interrogate the available evidence and design a practical path to an answer.
Strong intuition for data cleaning, ingestion and data modeling. We expect these foundations to be second nature so your thinking is free for larger and more ambiguous data initiatives, especially given the leverage of modern AI coding tools.
Comfortable building and deploying production data pipelines, not just analyzing data in notebooks or handing specifications to another engineering team.
Able to balance analytical depth with engineering pragmatism: you care whether an attribute is conceptually valid and whether it can be produced reliably at scale; you know when a new idea won't provide any lift.
Comfortable owning an ambiguous initiative end-to-end in a lean, fast-changing environment. You're more product-minded than people give you credit for.
Willingness to work in our New York City office. We provide a relocation package.
Eagerness to learn, grow and raise the bar with your coworkers.
Experience working with large, messy, multi-source datasets where the semantics were not obvious and documentation was incomplete.
Experience with consumer data, identity resolution, entity graphs, property data, behavioral or intent data, or other complex third-party data domains.
Experience with orchestration tools such as Dagster, Airflow or Prefect and transformation tools such as dbt or SQLMesh.
Experience with analytical databases such as Snowflake, Redshift or BigQuery and familiarity with transactional databases such as Postgres or MySQL.
Experience with lakehouse or distributed-processing systems such as Spark, Iceberg, Trino or AWS Glue.
Familiarity with AWS or another major cloud platform.
Exposure to ML eng/ops, applied ML or feature engineering. Deep modeling expertise is not required.
Effective use of AI coding tools such as Claude Code, Cursor or OpenCode as a force multiplier.
Prior experience at an early-stage startup.
You do not need to tick every box. If you are strong with data, we want to hear from you.
$170,000–$236,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nMinerva's data is not just infrastructure beneath our product; it is also one of our products. We are looking for a Data Engineer who can take ownership of complex consumer-data domains, develop a deep understanding of how their datasets relate and turn messy raw signals into trusted attributes and production data products. This role & Minerva are quite unique in the sense that GTM can immediately start selling your work output and generate enterprise-grade revenue in a matter of weeks.\n\nYou will spend most of your time at the transformation and derivation layer. You might become Minerva's internal expert on an identity graph, property and professional data, or a new source of consumer intent: learning the domain deeply enough to identify what is useful, what is misleading and what we should build next. Your job is not simply to make data available for someone downstream. You will use it to solve ambiguous problems and move an important part of our data product forward.\n\nThis is an end-to-end role. You will investigate novel datasets, source data when the answer is not already available, design domain models and derived attributes, and productionize your work so it can be consumed reliably by Minerva's applications, agents, APIs and ML models. Our data platform engineers build the infra that makes this work scalable; you must be comfortable operating within that infra and building your own ingestion and transformation pipelines without creating a bottleneck for the platform team.\n\nThe best fit can come from several backgrounds: a product-oriented data engineer, an applied data scientist who has data engineering skills, or a software engineer who has spent years solving difficult data problems. The common thread is first-principles reasoning, strong engineering fundamentals and a desire to own the answer from raw data through production. We test rigorously for data problem solving skills in our interview process.\n\n\nWHAT YOU'LL DO\n\n - Own one or more complex consumer-data domains end-to-end, becoming the person responsible for both understanding the data and advancing the products built from it.\n\n - Investigate large, messy and unfamiliar datasets. Establish their grain, keys, relationships, coverage, failure modes and fitness for different product use cases.\n\n - Design and build durable domain models, derived attributes and entity relationships that can power Minerva's applications, AI agents, APIs, customer deliveries and predictive models.\n\n - Build and operate the ingestion and transformation pipelines required to bring your work into production, including validation, observability, backfills and recovery. Productionization is key.\n\n - Go on data quests: identify and evaluate new sources, determine how they can improve our consumer graph and find clever ways to extract signal from imperfect inputs. We often have budget for purchasing new data when there is clear ROI.\n\n - Make data outputs trustworthy enough to be consumed autonomously. Define quality checks, provenance and guardrails that distinguish reliable signal from convenient but misleading data.\n\n - Partner with data scientists, platform engineers, product engineers and customer-facing teams to turn open-ended business or product questions into scalable data products.\n\n - Use LLMs, embeddings and modern AI development tools where they materially improve data standardization, classification, enrichment or engineering velocity.\n\n - Find new ways to create and protect business value through Minerva's proprietary data asset, from improving existing products to opening entirely new revenue opportunities.\n\n\nOUR DATA STACK\n\n - Dagster for orchestration\n\n - dbt-core within Dagster as a primary data-transformation surface\n\n - Snowflake for analytical workloads\n\n - Spark, Iceberg, Trino and AWS Glue for lakehouse workloads\n\n - Postgres, Elasticsearch and other product-facing systems\n\n - Frontier and open-source models, agent SDKs and batch APIs from OpenAI and Anthropic\n\n\nQUALIFICATIONS\n\n - 2-5+ years working as a data engineer, software engineer or applied data scientist in a data-heavy context. Your prior title matters less than evidence that you live and breathe data.\n\n - Highly proficient in Python and SQL.\n\n - Driven by first-principles thinking. You can take an ambiguous data problem, determine what must be true, interrogate the available evidence and design a practical path to an answer.\n\n - Strong intuition for data cleaning, ingestion and data modeling. We expect these foundations to be second nature so your thinking is free for larger and more ambiguous data initiatives, especially given the leverage of modern AI coding tools.\n\n - Comfortable building and deploying production data pipelines, not just analyzing data in notebooks or handing specifications to another engineering team.\n\n - Able to balance analytical depth with engineering pragmatism: you care whether an attribute is conceptually valid and whether it can be produced reliably at scale; you know when a new idea won't provide any lift.\n\n - Comfortable owning an ambiguous initiative end-to-end in a lean, fast-changing environment. You're more product-minded than people give you credit for.\n\n - Willingness to work in our New York City office. We provide a relocation package.\n\n - Eagerness to learn, grow and raise the bar with your coworkers.\n\n\nPREFERRED\n\n - Experience working with large, messy, multi-source datasets where the semantics were not obvious and documentation was incomplete.\n\n - Experience with consumer data, identity resolution, entity graphs, property data, behavioral or intent data, or other complex third-party data domains.\n\n - Experience with orchestration tools such as Dagster, Airflow or Prefect and transformation tools such as dbt or SQLMesh.\n\n - Experience with analytical databases such as Snowflake, Redshift or BigQuery and familiarity with transactional databases such as Postgres or MySQL.\n\n - Experience with lakehouse or distributed-processing systems such as Spark, Iceberg, Trino or AWS Glue.\n\n - Familiarity with AWS or another major cloud platform.\n\n - Exposure to ML eng/ops, applied ML or feature engineering. Deep modeling expertise is not required.\n\n - Effective use of AI coding tools such as Claude Code, Cursor or OpenCode as a force multiplier.\n\n - Prior experience at an early-stage startup.\n\nYou do not need to tick every box. If you are strong with data, we want to hear from you.\n\n\nCOMPENSATION\n\n$170,000–$236,000 base salary, commensurate with experience. Competitive equity. Medical, dental and vision.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"d2d11ac5-09fb-43bc-b310-34161f16ee16","title":"Forward Deployed Marketer","department":"Solutions","team":"Solutions","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-01T20:42:55.338+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/d2d11ac5-09fb-43bc-b310-34161f16ee16","applyUrl":"https://jobs.ashbyhq.com/minerva/d2d11ac5-09fb-43bc-b310-34161f16ee16/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
An agent that can do almost anything has the same problem as a blank page. Someone has to decide what it does first, and then build it so a marketing team actually uses it.
That’s this seat. You bring a marketer’s judgment and an engineer’s build skills, and you do the work inside our customers’ marketing teams. You scope every pilot and pick the campaign that will move a real number. Then you turn it into a working system on Minerva (the agents, the data, the audience, the measurement), and you show the customer everything else Minerva can do, so the next request comes from them.
You decide which workflows are worth building and which aren’t. Nobody files a ticket for you.
This is a senior individual contributor seat with no direct reports. You’ll work directly with the founders, alongside our account executives, account managers, solutions engineers and forward deployed engineers.
Scope every pilot. Sit in pre-sale with the AE and pick the use case that will move a brand-side number inside the trial window.
Turn the use case into a system. Break the customer’s workflow down: what the agents do, which data they read, what they hand back, where someone on the customer’s team signs off, and how you’ll know when the output starts to drift.
Ship systems, not demos. A use case is done when their team runs it every week and it holds up on their messy data. You’re done when they run the second one without you.
Make the call when a number isn’t moving. Every brand’s first-party data is shaped by its own history. You tell a data problem from a definition problem, send the first to the account’s solutions engineer or forward deployed engineer and fix the second yourself.
Build what they didn’t know to ask for. A lapsed-buyer audience that rebuilds itself from the order table every week and reports lift against a holdout. A renewal-risk list a ticketing team works from every Monday. When we have no play for it yet, you design it and the account team sells it. The account manager owns the relationship and renewal.
Make yourself unnecessary. For each vertical, write down which use case goes first, which audience definitions survive a real campaign and what the VP needs to see in week two. Then record the Loom and run the session, so their team and our AEs can run it without you.
Go back when the tools get better. The models and our product improve every month. Revisit the accounts you’ve already deployed and rebuild what’s now possible.
Feed product the specific thing. What you reached for in the middle of real work shapes what we build next.
You’ve owned a brand’s marketing outcome. A campaign or a whole function, with the target and the result in your name. Agency-side counts if the client’s number was yours. Supporting the person who owned it doesn’t.
You’ve shipped something a marketing team adopted. An agent or an internal tool that got past the demo: people used it every week and you can tie it to a number they cared about.
You build with AI yourself. Hands-on with model APIs or agent tools: prompt design, structured outputs, evals that tell you when it’s wrong. You work in things like Claude Code, Cursor, n8n, Lovable or Vercel. We test this live in the loop.
You understand the full cycle, from strategy through audience, activation and measurement, and you can speak to every stage without handing off.
You know enough about data to diagnose. You don’t need to fix a broken pipeline, but you can tell when one is the problem.
Located in or willing to relocate to New York City.
A marketing engineer, growth engineer or GTM engineer who came up as a marketer and wants a customer’s number in their name.
A client lead at a performance or lifecycle agency who has owned outcomes across several brands and is tired of handing off the recommendation.
A growth or lifecycle lead at a consumer brand who has run the full cycle on one brand’s data and wants to do it across many.
A solutions lead at a marketing data platform who came up in marketing and wants to own the outcome, not the implementation.
Or you don’t fit any of these neatly, but you can name the number you moved for a brand and show us something you built. Apply.
SQL, even basic. This is the highest-slope accelerant for this seat.
Time at a CDP, identity or martech company, on either side of the table.
First 90 days: you’re on every pilot scoping call. At least one brand-side number has moved because of a system you designed and their team now runs, and one vertical has a written playbook.
First 6 months: you can name the number you moved on every account you deployed this quarter. At least one expansion exists because you built something new. The roadmap carries items that trace back to you.
The number you own: brand-side outcomes moved per deployed account, per quarter.
$200,000–$240,000 base salary, depending on experience. Early-stage equity. Medical, dental and vision.
Skip the cover letter. Send us one marketing outcome you owned, for a campaign or a whole function: the number, what you built and what happened. If you were agency-side, tell us the client’s number and which part of it was yours. Applications without a number won’t be reviewed. If you’ve built something, link it. A repo or a Loom is plenty. Later in the process you’ll get a Minerva login and design a use case for a real account, from the audience through to how you’d measure it.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nTHE ROLE\n\nAn agent that can do almost anything has the same problem as a blank page. Someone has to decide what it does first, and then build it so a marketing team actually uses it.\n\nThat’s this seat. You bring a marketer’s judgment and an engineer’s build skills, and you do the work inside our customers’ marketing teams. You scope every pilot and pick the campaign that will move a real number. Then you turn it into a working system on Minerva (the agents, the data, the audience, the measurement), and you show the customer everything else Minerva can do, so the next request comes from them.\n\nYou decide which workflows are worth building and which aren’t. Nobody files a ticket for you.\n\nThis is a senior individual contributor seat with no direct reports. You’ll work directly with the founders, alongside our account executives, account managers, solutions engineers and forward deployed engineers.\n\n\nWHAT YOU'LL DO\n\n - Scope every pilot. Sit in pre-sale with the AE and pick the use case that will move a brand-side number inside the trial window.\n\n - Turn the use case into a system. Break the customer’s workflow down: what the agents do, which data they read, what they hand back, where someone on the customer’s team signs off, and how you’ll know when the output starts to drift.\n\n - Ship systems, not demos. A use case is done when their team runs it every week and it holds up on their messy data. You’re done when they run the second one without you.\n\n - Make the call when a number isn’t moving. Every brand’s first-party data is shaped by its own history. You tell a data problem from a definition problem, send the first to the account’s solutions engineer or forward deployed engineer and fix the second yourself.\n\n - Build what they didn’t know to ask for. A lapsed-buyer audience that rebuilds itself from the order table every week and reports lift against a holdout. A renewal-risk list a ticketing team works from every Monday. When we have no play for it yet, you design it and the account team sells it. The account manager owns the relationship and renewal.\n\n - Make yourself unnecessary. For each vertical, write down which use case goes first, which audience definitions survive a real campaign and what the VP needs to see in week two. Then record the Loom and run the session, so their team and our AEs can run it without you.\n\n - Go back when the tools get better. The models and our product improve every month. Revisit the accounts you’ve already deployed and rebuild what’s now possible.\n\n - Feed product the specific thing. What you reached for in the middle of real work shapes what we build next.\n\n\nWHAT YOU HAVE\n\n - You’ve owned a brand’s marketing outcome. A campaign or a whole function, with the target and the result in your name. Agency-side counts if the client’s number was yours. Supporting the person who owned it doesn’t.\n\n - You’ve shipped something a marketing team adopted. An agent or an internal tool that got past the demo: people used it every week and you can tie it to a number they cared about.\n\n - You build with AI yourself. Hands-on with model APIs or agent tools: prompt design, structured outputs, evals that tell you when it’s wrong. You work in things like Claude Code, Cursor, n8n, Lovable or Vercel. We test this live in the loop.\n\n - You understand the full cycle, from strategy through audience, activation and measurement, and you can speak to every stage without handing off.\n\n - You know enough about data to diagnose. You don’t need to fix a broken pipeline, but you can tell when one is the problem.\n\n - Located in or willing to relocate to New York City.\n\n\nYOU MIGHT BE A FIT IF YOU ARE…\n\n - A marketing engineer, growth engineer or GTM engineer who came up as a marketer and wants a customer’s number in their name.\n\n - A client lead at a performance or lifecycle agency who has owned outcomes across several brands and is tired of handing off the recommendation.\n\n - A growth or lifecycle lead at a consumer brand who has run the full cycle on one brand’s data and wants to do it across many.\n\n - A solutions lead at a marketing data platform who came up in marketing and wants to own the outcome, not the implementation.\n\n - Or you don’t fit any of these neatly, but you can name the number you moved for a brand and show us something you built. Apply.\n\n\nNICE TO HAVES\n\n - SQL, even basic. This is the highest-slope accelerant for this seat.\n\n - Time at a CDP, identity or martech company, on either side of the table.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: you’re on every pilot scoping call. At least one brand-side number has moved because of a system you designed and their team now runs, and one vertical has a written playbook.\n\n - First 6 months: you can name the number you moved on every account you deployed this quarter. At least one expansion exists because you built something new. The roadmap carries items that trace back to you.\n\n - The number you own: brand-side outcomes moved per deployed account, per quarter.\n\n\nCOMPENSATION\n\n$200,000–$240,000 base salary, depending on experience. Early-stage equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Send us one marketing outcome you owned, for a campaign or a whole function: the number, what you built and what happened. If you were agency-side, tell us the client’s number and which part of it was yours. Applications without a number won’t be reviewed. If you’ve built something, link it. A repo or a Loom is plenty. Later in the process you’ll get a Minerva login and design a use case for a real account, from the audience through to how you’d measure it.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"59b2ff01-1fb7-41e7-9f67-974916980689","title":"Strategic Finance Lead","department":"G&A","team":"Finance","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T16:05:27.283+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/59b2ff01-1fb7-41e7-9f67-974916980689","applyUrl":"https://jobs.ashbyhq.com/minerva/59b2ff01-1fb7-41e7-9f67-974916980689/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
You’re Minerva’s first strategic finance hire. You have maximum exposure to the key business bottlenecks that the executive team grapples with on a day to day basis. Ideally you’re able to identify and take the first pass on solving many of these problems.
You sit within the operations org and work with the CEO, Chief-of-Staff, VP of Ops and broader leadership team to prioritize analyses. You prepare models that help guide capital allocation and real resource allocation.
This is a generalist role with broad scope - spanning planning, operations, go-to-market, pricing, board and investor reporting and special projects.
Own the core financial models that guide capital allocation and resource planning.
Design incentive systems that improve employee performance, retention and satisfaction.
Design commercial policies (pricing, packaging, contract terms) that deeply align our incentives with our customers while building a sound financial foundation across products.
Optimize A/P and A/R policies with Accounting / RevOps.
Partner with CoS to own all board, investor, and internal reporting materials, turning complex data into clear narratives that drive decisions.
Work with the CEO & CoS to develop operating KPIs and reporting systems.
Build business cases for strategic decisions including deal structuring, investments, hiring plans, and product launches.
Extreme agency, candor and rigor
4-7+ years of experience in strategic finance, investment banking, private equity, growth equity, or a similar high-rigor analytical role
Corporate development exposure is a plus but is not required.
Strong financial modeling skills and the ability to build complex operating models from scratch, including experience with unit economics and cost structure analysis
Comfort with ambiguity and a bias toward ownership
Willingness to match or outpace the hours and intensity of the leadership team
More likely to answer a question by finding the person who has already solved the problem than deriving an answer from first principles
Willingness to have an opinion and share it
Enthusiasm for AI and Minerva’s mission
Base salary: $175,000–$300,000, depending on seniority. Significant equity exposure.
Skip the cover letter. Send us one model you built and the decision it changed: the question, how you structured it, the answer and what happened. If the model is confidential, describe the structure.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nABOUT THE ROLE\n\nYou’re Minerva’s first strategic finance hire. You have maximum exposure to the key business bottlenecks that the executive team grapples with on a day to day basis. Ideally you’re able to identify and take the first pass on solving many of these problems.\n\nYou sit within the operations org and work with the CEO, Chief-of-Staff, VP of Ops and broader leadership team to prioritize analyses. You prepare models that help guide capital allocation and real resource allocation.\n\nThis is a generalist role with broad scope - spanning planning, operations, go-to-market, pricing, board and investor reporting and special projects.\n\n\nWHAT YOU WILL DO\n\n - Own the core financial models that guide capital allocation and resource planning.\n\n - Design incentive systems that improve employee performance, retention and satisfaction.\n\n - Design commercial policies (pricing, packaging, contract terms) that deeply align our incentives with our customers while building a sound financial foundation across products.\n\n - Optimize A/P and A/R policies with Accounting / RevOps.\n\n - Partner with CoS to own all board, investor, and internal reporting materials, turning complex data into clear narratives that drive decisions.\n\n - Work with the CEO & CoS to develop operating KPIs and reporting systems.\n\n - Build business cases for strategic decisions including deal structuring, investments, hiring plans, and product launches.\n\n\nIDEAL QUALIFICATIONS\n\n - Extreme agency, candor and rigor\n\n - 4-7+ years of experience in strategic finance, investment banking, private equity, growth equity, or a similar high-rigor analytical role\n\n - Corporate development exposure is a plus but is not required.\n\n - Strong financial modeling skills and the ability to build complex operating models from scratch, including experience with unit economics and cost structure analysis\n\n - Comfort with ambiguity and a bias toward ownership\n\n - Willingness to match or outpace the hours and intensity of the leadership team\n\n - More likely to answer a question by finding the person who has already solved the problem than deriving an answer from first principles\n\n - Willingness to have an opinion and share it\n\n - Enthusiasm for AI and Minerva’s mission\n\n\nCOMPENSATION\n\nBase salary: $175,000–$300,000, depending on seniority. Significant equity exposure.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Send us one model you built and the decision it changed: the question, how you structured it, the answer and what happened. If the model is confidential, describe the structure.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"b06da930-2eac-4eb2-b8ba-78868cda2889","title":"Senior Design Engineer","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T16:10:58.170+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/b06da930-2eac-4eb2-b8ba-78868cda2889","applyUrl":"https://jobs.ashbyhq.com/minerva/b06da930-2eac-4eb2-b8ba-78868cda2889/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
We build in TypeScript, React, TanStack Start, Base UI and Tailwind. We use Motion where movement earns its place.
Design happens in code first. Figma or Paper comes out when a sketch is faster.
Our users are marketers and the CMOs they report to. They work with consumer data at a scale most interfaces never have to carry.
An agent can build a segment in minutes. A marketer still has to understand it, trust it and put budget behind it. Getting them there is an interface problem, and it's this job.
Minerva's agents handle a marketing team's most data-heavy work. What reaches the customer is a screen. That screen has to make complex data feel simple. It also has to make the output trustworthy, and good enough to use, so that it leads to a result.
A design engineer here doesn't sit between design and engineering. You own the quality of the product experience from concept to shipped software, and you use code as the design medium. The distinction is ownership, not skill overlap.
This is a senior individual contributor seat on the product engineering team.
Make complex data legible. Design and build the surfaces where marketers work with data on hundreds of millions of consumers. Understanding an audience should take seconds, not a data team.
Turn agent output into a decision. Decide how segments, models and measurement are shown and explained, so a marketer trusts them, enjoys using them and puts them in market.
Design in code. Prototype what a static mock can't express, like motion, keyboard flows and work an agent is still doing. Take the strongest version to production yourself.
Make the cost call. Decide when an animation earns its place, and when a component is a one-off versus a primitive the team builds on.
Own the design system. Build and maintain the components and patterns product engineering ships with, so every surface feels like the same product.
Own it after launch. You're accountable for how a surface feels in a customer's hands, not how it looked in review.
Taste. This is the hardest one to write a bullet for, so we'll just say it: your work shows it. Sometimes it's restraint. Sometimes it's making dull data fun, getting clarity out of a user, or showing what a product does without giving away how.
Opinions you can defend. Your reasons come from first principles that matter to the person using the product, not from a model's suggestion.
Shipped work. You've designed and shipped production interfaces yourself. We don't care about titles or years. We care what you've built.
You fight for decisions, with others. You argue a call through with people who care as much as you do, and you let the better argument win.
Located in or willing to relocate to New York City.
First 90 days: you've shipped your first major product surface end to end, and it's yours.
First 6 months: you've shipped and own several of the bigger product surfaces, and the design system is what product engineering builds with by default.
There's no single number. The measure is the surfaces you own and how they hold up in customers' hands.
$237,000–$265,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Send us your portfolio link, and make sure it includes something you designed and shipped. Applications without one won't be reviewed.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\n\nDESIGN ENGINEERING AT MINERVA\n\n - We build in TypeScript, React, TanStack Start, Base UI and Tailwind. We use Motion where movement earns its place.\n\n - Design happens in code first. Figma or Paper comes out when a sketch is faster.\n\n - Our users are marketers and the CMOs they report to. They work with consumer data at a scale most interfaces never have to carry.\n\n\nTHE ROLE\n\nAn agent can build a segment in minutes. A marketer still has to understand it, trust it and put budget behind it. Getting them there is an interface problem, and it's this job.\n\nMinerva's agents handle a marketing team's most data-heavy work. What reaches the customer is a screen. That screen has to make complex data feel simple. It also has to make the output trustworthy, and good enough to use, so that it leads to a result.\n\nA design engineer here doesn't sit between design and engineering. You own the quality of the product experience from concept to shipped software, and you use code as the design medium. The distinction is ownership, not skill overlap.\n\nThis is a senior individual contributor seat on the product engineering team.\n\n \n\n\nWHAT YOU'LL DO\n\n - Make complex data legible. Design and build the surfaces where marketers work with data on hundreds of millions of consumers. Understanding an audience should take seconds, not a data team.\n\n - Turn agent output into a decision. Decide how segments, models and measurement are shown and explained, so a marketer trusts them, enjoys using them and puts them in market.\n\n - Design in code. Prototype what a static mock can't express, like motion, keyboard flows and work an agent is still doing. Take the strongest version to production yourself.\n\n - Make the cost call. Decide when an animation earns its place, and when a component is a one-off versus a primitive the team builds on.\n\n - Own the design system. Build and maintain the components and patterns product engineering ships with, so every surface feels like the same product.\n\n - Own it after launch. You're accountable for how a surface feels in a customer's hands, not how it looked in review.\n\n\nWHAT YOU HAVE\n\n - Taste. This is the hardest one to write a bullet for, so we'll just say it: your work shows it. Sometimes it's restraint. Sometimes it's making dull data fun, getting clarity out of a user, or showing what a product does without giving away how.\n\n - Opinions you can defend. Your reasons come from first principles that matter to the person using the product, not from a model's suggestion.\n\n - Shipped work. You've designed and shipped production interfaces yourself. We don't care about titles or years. We care what you've built.\n\n - You fight for decisions, with others. You argue a call through with people who care as much as you do, and you let the better argument win.\n\n - Located in or willing to relocate to New York City.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: you've shipped your first major product surface end to end, and it's yours.\n\n - First 6 months: you've shipped and own several of the bigger product surfaces, and the design system is what product engineering builds with by default.\n\n - There's no single number. The measure is the surfaces you own and how they hold up in customers' hands.\n\n\nCOMPENSATION\n\n$237,000–$265,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.\n\n \n\n\nHOW TO APPLY\n\nSkip the cover letter. Send us your portfolio link, and make sure it includes something you designed and shipped. Applications without one won't be reviewed.\n\n \n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"735ef16e-ff0a-4387-a869-34c0adafded8","title":"Solutions Engineer","department":"Solutions","team":"Solutions","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T16:18:09.706+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/735ef16e-ff0a-4387-a869-34c0adafded8","applyUrl":"https://jobs.ashbyhq.com/minerva/735ef16e-ff0a-4387-a869-34c0adafded8/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Every enterprise account moves from a data test to a pilot to production. The technical win usually decides the deal.
Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.
Every deal reaches the moment when the buyer's data team asks how it actually works. Every new customer reaches the moment their data has to land in Minerva and come out scored. Today our founders and data leads handle both, and they sit on the critical path of every deal.
You take both moments. You run the demo and the technical evaluation, then you run the onboarding: data in, scoring live, the customer's team trained on the platform. Most of our enterprise customers come through you. The few that need custom pipelines or models go to our forward deployed engineers, and you know when to hand one off.
Run demos and technical evaluations, often on the buyer's own data: discovery with their data team, how the data moves (warehouse shares, flat files, API) and a match test on their sample.
Run enterprise onboarding. Ingest first-party data, run routine scoring from our admin dashboard and get the customer's team live in the platform. Think of a travel brand's CRM export, from first file to first scored audience.
Build the simple pipelines and transformations onboarding needs, in SQL and light Python.
Carry the technical side of security reviews with our compliance lead, so questionnaires don't stall deals.
Write the playbook. Standard onboarding steps, reference setups and demo environments, so the next SE ramps in weeks.
Bring back what buyers asked for that we couldn't show, and tell product which gaps cost deals.
5+ years in solutions engineering, solutions architecture, customer data onboarding or technical consulting, ideally at a martech, adtech, CDP, identity or analytics company, or an agency's data team.
Strong SQL. You've worked hands-on with customer data and the platforms it lives in.
You've run a demo for a skeptical data team and an onboarding for an enterprise customer, and both went well.
You're customer-centric to the core. You see every problem from the customer's side first.
You're calm and clear in a room of engineers looking for the hole.
You're in New York City or willing to relocate.
Some data science or data engineering background, enough to have opinions about how an experiment should be set up.
Snowflake, BigQuery or Databricks, and how companies share data out of them.
Consumer data, identity resolution or marketing analytics.
Sports, financial services or travel.
Custom modeling. When an account needs a custom pipeline or model, our forward deployed engineers build it, and you scope the hand-off.
Sales. The account executive owns the deal. You own whether it works.
First 90 days: you've run the technical evaluation and the onboarding on every account assigned to you without a founder in the room, and the first version of the onboarding playbook exists.
First 6 months: time from signed to first scored audience has a number and keeps falling. Every account you handed to an FDE went over with a scope they accepted as written.
The numbers you own: technical win rate on your evaluations and time-to-live on your onboardings.
$215,000–$241,000 base salary, depending on experience, plus a variable bonus targeted at 10% of base. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Walk us through one customer onboarding or technical evaluation you ran: the customer's data setup, what you built or checked, what nearly broke it and how it ended. A few paragraphs.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nHOW WE DEPLOY\n\n - Every enterprise account moves from a data test to a pilot to production. The technical win usually decides the deal.\n\n - Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.\n\n\nTHE ROLE\n\nEvery deal reaches the moment when the buyer's data team asks how it actually works. Every new customer reaches the moment their data has to land in Minerva and come out scored. Today our founders and data leads handle both, and they sit on the critical path of every deal.\n\nYou take both moments. You run the demo and the technical evaluation, then you run the onboarding: data in, scoring live, the customer's team trained on the platform. Most of our enterprise customers come through you. The few that need custom pipelines or models go to our forward deployed engineers, and you know when to hand one off.\n\n\nWHAT YOU'LL DO\n\n - Run demos and technical evaluations, often on the buyer's own data: discovery with their data team, how the data moves (warehouse shares, flat files, API) and a match test on their sample.\n\n - Run enterprise onboarding. Ingest first-party data, run routine scoring from our admin dashboard and get the customer's team live in the platform. Think of a travel brand's CRM export, from first file to first scored audience.\n\n - Build the simple pipelines and transformations onboarding needs, in SQL and light Python.\n\n - Carry the technical side of security reviews with our compliance lead, so questionnaires don't stall deals.\n\n - Write the playbook. Standard onboarding steps, reference setups and demo environments, so the next SE ramps in weeks.\n\n - Bring back what buyers asked for that we couldn't show, and tell product which gaps cost deals.\n\n\nWHAT YOU HAVE\n\n - 5+ years in solutions engineering, solutions architecture, customer data onboarding or technical consulting, ideally at a martech, adtech, CDP, identity or analytics company, or an agency's data team.\n\n - Strong SQL. You've worked hands-on with customer data and the platforms it lives in.\n\n - You've run a demo for a skeptical data team and an onboarding for an enterprise customer, and both went well.\n\n - You're customer-centric to the core. You see every problem from the customer's side first.\n\n - You're calm and clear in a room of engineers looking for the hole.\n\n - You're in New York City or willing to relocate.\n\n\nNICE TO HAVES\n\n - Some data science or data engineering background, enough to have opinions about how an experiment should be set up.\n\n - Snowflake, BigQuery or Databricks, and how companies share data out of them.\n\n - Consumer data, identity resolution or marketing analytics.\n\n - Sports, financial services or travel.\n\n\nWHAT THIS ISN'T\n\n - Custom modeling. When an account needs a custom pipeline or model, our forward deployed engineers build it, and you scope the hand-off.\n\n - Sales. The account executive owns the deal. You own whether it works.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: you've run the technical evaluation and the onboarding on every account assigned to you without a founder in the room, and the first version of the onboarding playbook exists.\n\n - First 6 months: time from signed to first scored audience has a number and keeps falling. Every account you handed to an FDE went over with a scope they accepted as written.\n\n - The numbers you own: technical win rate on your evaluations and time-to-live on your onboardings.\n\n\nCOMPENSATION\n\n$215,000–$241,000 base salary, depending on experience, plus a variable bonus targeted at 10% of base. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Walk us through one customer onboarding or technical evaluation you ran: the customer's data setup, what you built or checked, what nearly broke it and how it ended. A few paragraphs.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"5edf818e-6dac-4ec4-8cd2-0659259249eb","title":"Applied AI Engineer, Internal Systems","department":"G&A","team":"Business Operations","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T16:20:44.729+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/5edf818e-6dac-4ec4-8cd2-0659259249eb","applyUrl":"https://jobs.ashbyhq.com/minerva/5edf818e-6dac-4ec4-8cd2-0659259249eb/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Agents: Claude across the company (in Slack, through the API, with skills and MCP), plus Mastra for the agents we build ourselves and Temporal for long-running work.
Code: Python and TypeScript, scheduled jobs and webhooks.
The systems your agents work across: Slack, Google Workspace, Notion, and the CRM, recruiting, HR, finance and compliance tools a growing company runs on.
We build agents for marketers. This seat builds them for us.
Every company our size runs on work nobody sees. Someone copies a new hire's details into six tools. Someone remembers which contract renews next month. Someone answers the same security questionnaire for the fourth time. Most companies hire a coordinator for that work. We're hiring an engineer.
You're a forward-deployed engineer for Minerva itself. Other companies call this an AI operations engineer or an internal AI engineer. Your customers are every team here: operations, finance, people, recruiting, sales and the founders. You sit with them, find the work they'd most like to stop doing, and replace it with a system that runs without them and leaves an audit trail.
You report to our COO and work closely with engineering, which owns anything that touches production.
Own the company's knowledge agent. One agent over our docs, Slack, calls and tickets, with Slack as the front end. It answers with sources, and people trust it.
Automate the processes that run the company. Hiring, onboarding and offboarding, access provisioning, vendor reviews, customer security questionnaires, contract renewals and billing. Start where the hours or the risk are highest.
Run the skill library. Every team gets Claude connected to the tools it uses and a set of skills, each with a named owner on that team, an eval and the right permissions. What one person figures out, everyone gets.
Own the source of truth. Decide where each kind of information lives, move it there and keep it findable by people and by agents. When an agent spots something out of date, it proposes the fix and a person approves it.
Build the plumbing under our operating dashboards: the metrics, controls and board reporting that today get stitched together by hand from five systems.
Put the guardrails in first. Least-privilege access, a human approval before any agent writes to a system of record, and audit trails that double as compliance evidence.
Sit with your users. Watch how a recruiter, a seller or our finance lead actually works, then automate the part they hate.
Measure adoption. Which systems get used, by whom and at what cost, including what we spend on models and which agents can touch what. Retire what nobody opens, and write the runbook for what stays.
You've shipped agents or automations that coworkers use every week, and you can show the usage: who stopped doing what, and how many hours came back.
Production Python or TypeScript, with LLMs wired into real APIs. You're comfortable with OAuth scopes, webhooks and scheduled jobs, and with the ways each of them fails.
You know how a company runs. You can talk to a recruiter, a controller and a security auditor in their own terms, and you fix the process before you automate it.
A controls instinct. Before you ship, you've decided who approves what and what happens when the agent is wrong.
You're current. You know what shipped in AI tooling this month, and you can show us your own setup.
You write clearly. People follow your runbooks without asking you.
You're in New York City or willing to relocate.
A founder or founding engineer who automated your own company's back office because someone had to.
A chief of staff or business operations lead who learned to build and stopped writing specs for engineers.
A solutions or forward deployed engineer who wants the whole company as your customer.
A GTM or internal tools engineer who automated a sales team and wants scope beyond the sales stack.
Or you don't fit any of these neatly, but you've automated your own job at least once. Apply.
You've rolled out Claude or another model platform to a whole company: access, permissions, usage and cost.
You've been through a SOC 2 or similar audit on the side that produces the evidence.
Hands-on time with the systems a startup runs on: HRIS, ATS, CRM, billing and spend tools.
An IT helpdesk. You'll automate the tickets away, not answer them.
Product engineering. Our customer-facing agents and production infrastructure belong to the engineering team.
An AI strategy role. You ship systems people use. Nobody here needs a deck about transformation.
The owner of the work you automate. Our compliance lead still owns the security answers, and whoever runs sales ops owns the CRM. You build the systems that do the work for them.
First 90 days: the knowledge agent is live in Slack and used daily. A new hire gets set up in every tool in under an hour, and the access record writes itself. At least three workflows run without a person in the loop and are still in use four weeks later.
First 6 months: security questionnaires go out the same day without a founder writing them. Contract renewals, notice windows and price increases surface before they cost us money. Every team has a system it would fight to keep.
The number you own: hours of manual work retired each month, measured against a baseline you set in your first month.
$211,000–$236,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Show us one thing you built that other people used: a demo, a repo, or a few paragraphs on what the process looked like before, what you built, how many people used it and what broke. Applications without one won't be reviewed.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nOUR STACK\n\n - Agents: Claude across the company (in Slack, through the API, with skills and MCP), plus Mastra for the agents we build ourselves and Temporal for long-running work.\n\n - Code: Python and TypeScript, scheduled jobs and webhooks.\n\n - The systems your agents work across: Slack, Google Workspace, Notion, and the CRM, recruiting, HR, finance and compliance tools a growing company runs on.\n\n\nTHE ROLE\n\nWe build agents for marketers. This seat builds them for us.\n\nEvery company our size runs on work nobody sees. Someone copies a new hire's details into six tools. Someone remembers which contract renews next month. Someone answers the same security questionnaire for the fourth time. Most companies hire a coordinator for that work. We're hiring an engineer.\n\nYou're a forward-deployed engineer for Minerva itself. Other companies call this an AI operations engineer or an internal AI engineer. Your customers are every team here: operations, finance, people, recruiting, sales and the founders. You sit with them, find the work they'd most like to stop doing, and replace it with a system that runs without them and leaves an audit trail.\n\nYou report to our COO and work closely with engineering, which owns anything that touches production.\n\n\nWHAT YOU'LL DO\n\n - Own the company's knowledge agent. One agent over our docs, Slack, calls and tickets, with Slack as the front end. It answers with sources, and people trust it.\n\n - Automate the processes that run the company. Hiring, onboarding and offboarding, access provisioning, vendor reviews, customer security questionnaires, contract renewals and billing. Start where the hours or the risk are highest.\n\n - Run the skill library. Every team gets Claude connected to the tools it uses and a set of skills, each with a named owner on that team, an eval and the right permissions. What one person figures out, everyone gets.\n\n - Own the source of truth. Decide where each kind of information lives, move it there and keep it findable by people and by agents. When an agent spots something out of date, it proposes the fix and a person approves it.\n\n - Build the plumbing under our operating dashboards: the metrics, controls and board reporting that today get stitched together by hand from five systems.\n\n - Put the guardrails in first. Least-privilege access, a human approval before any agent writes to a system of record, and audit trails that double as compliance evidence.\n\n - Sit with your users. Watch how a recruiter, a seller or our finance lead actually works, then automate the part they hate.\n\n - Measure adoption. Which systems get used, by whom and at what cost, including what we spend on models and which agents can touch what. Retire what nobody opens, and write the runbook for what stays.\n\n\nWHAT YOU HAVE\n\n - You've shipped agents or automations that coworkers use every week, and you can show the usage: who stopped doing what, and how many hours came back.\n\n - Production Python or TypeScript, with LLMs wired into real APIs. You're comfortable with OAuth scopes, webhooks and scheduled jobs, and with the ways each of them fails.\n\n - You know how a company runs. You can talk to a recruiter, a controller and a security auditor in their own terms, and you fix the process before you automate it.\n\n - A controls instinct. Before you ship, you've decided who approves what and what happens when the agent is wrong.\n\n - You're current. You know what shipped in AI tooling this month, and you can show us your own setup.\n\n - You write clearly. People follow your runbooks without asking you.\n\n - You're in New York City or willing to relocate.\n\n\nYOU MIGHT BE A FIT IF YOU ARE…\n\n - A founder or founding engineer who automated your own company's back office because someone had to.\n\n - A chief of staff or business operations lead who learned to build and stopped writing specs for engineers.\n\n - A solutions or forward deployed engineer who wants the whole company as your customer.\n\n - A GTM or internal tools engineer who automated a sales team and wants scope beyond the sales stack.\n\n - Or you don't fit any of these neatly, but you've automated your own job at least once. Apply.\n\n\nNICE TO HAVES\n\n - You've rolled out Claude or another model platform to a whole company: access, permissions, usage and cost.\n\n - You've been through a SOC 2 or similar audit on the side that produces the evidence.\n\n - Hands-on time with the systems a startup runs on: HRIS, ATS, CRM, billing and spend tools.\n\n\nWHAT THIS ISN'T\n\n - An IT helpdesk. You'll automate the tickets away, not answer them.\n\n - Product engineering. Our customer-facing agents and production infrastructure belong to the engineering team.\n\n - An AI strategy role. You ship systems people use. Nobody here needs a deck about transformation.\n\n - The owner of the work you automate. Our compliance lead still owns the security answers, and whoever runs sales ops owns the CRM. You build the systems that do the work for them.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: the knowledge agent is live in Slack and used daily. A new hire gets set up in every tool in under an hour, and the access record writes itself. At least three workflows run without a person in the loop and are still in use four weeks later.\n\n - First 6 months: security questionnaires go out the same day without a founder writing them. Contract renewals, notice windows and price increases surface before they cost us money. Every team has a system it would fight to keep.\n\n - The number you own: hours of manual work retired each month, measured against a baseline you set in your first month.\n\n\nCOMPENSATION\n\n$211,000–$236,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Show us one thing you built that other people used: a demo, a repo, or a few paragraphs on what the process looked like before, what you built, how many people used it and what broke. Applications without one won't be reviewed.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"c6656168-ea57-4038-a276-54766dafc980","title":"Operations Associate","department":"G&A","team":"Business Operations","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T16:22:59.305+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/c6656168-ea57-4038-a276-54766dafc980","applyUrl":"https://jobs.ashbyhq.com/minerva/c6656168-ea57-4038-a276-54766dafc980/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
We're growing fast across New York and several countries, and every new person, vendor and candidate runs through operations. You keep that machine running and make it run better every month.
This is a generalist seat on a small operations team. You'll run the processes that touch everyone at Minerva: access and equipment, vendors and contracts, and the written procedures behind all of it. You'll also run the logistics for new-hire onboarding and interview scheduling, with our Head of Ecosystem, who owns the new-hire experience and our recruiting process. You'll work next to an engineer whose job is to automate this work, so part of your job is showing them what to automate next.
You report to our COO.
Keep access and devices in order. Who has which tool, and who doesn't anymore, tracked well enough to hand to an auditor.
Run onboarding and offboarding logistics. Accounts, equipment and background checks for hires in New York and abroad, so every first day starts ready.
Keep interviews moving. Schedule loops and chase feedback, so no candidate waits on us.
Manage vendors and renewals. Track contracts and renewal dates, run the intake for new tools, and chase what's overdue.
Write the playbook. Every recurring process gets a documented, current procedure someone else could follow.
Find the next thing to automate. You'll know which steps waste the most time, because you're the one doing them.
1–3 years in operations, people operations, recruiting coordination, office or business operations, ideally at a startup or another fast, scrappy team.
You finish things. Nothing you own falls through.
You're organized enough that a process you run is easy to audit and easy to hand off.
You write clearly and keep people informed without being asked.
You use AI tools in your own work every day and you're curious about doing more with them.
Discretion with personal and candidate information.
You're in New York City or willing to relocate.
A recruiting coordinator who wants a bigger operations seat.
An operations or office lead at a small company who did a bit of everything.
An analyst or consultant who'd rather run things than recommend them.
Or you don't fit any of these neatly, but you've made a messy process run well. Apply.
First 90 days: every new hire's first day is set up before they arrive, every interview loop has its feedback in on time, and the access list is current.
First 6 months: the recurring processes you run are all written down, and at least three of them have been automated with the engineer, on your recommendation.
$95,000–$121,000 base salary, depending on experience. Equity. Medical, dental and vision.
Skip the cover letter. Tell us about a messy, recurring process you took over and made run better: what was broken, what you changed and what it looked like a month later. A few paragraphs.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\n\nTHE ROLE\n\nWe're growing fast across New York and several countries, and every new person, vendor and candidate runs through operations. You keep that machine running and make it run better every month.\n\nThis is a generalist seat on a small operations team. You'll run the processes that touch everyone at Minerva: access and equipment, vendors and contracts, and the written procedures behind all of it. You'll also run the logistics for new-hire onboarding and interview scheduling, with our Head of Ecosystem, who owns the new-hire experience and our recruiting process. You'll work next to an engineer whose job is to automate this work, so part of your job is showing them what to automate next.\n\nYou report to our COO.\n\n\n\nWHAT YOU'LL DO\n\n - Keep access and devices in order. Who has which tool, and who doesn't anymore, tracked well enough to hand to an auditor.\n\n - Run onboarding and offboarding logistics. Accounts, equipment and background checks for hires in New York and abroad, so every first day starts ready.\n\n - Keep interviews moving. Schedule loops and chase feedback, so no candidate waits on us.\n\n - Manage vendors and renewals. Track contracts and renewal dates, run the intake for new tools, and chase what's overdue.\n\n - Write the playbook. Every recurring process gets a documented, current procedure someone else could follow.\n\n - Find the next thing to automate. You'll know which steps waste the most time, because you're the one doing them.\n\n\nWHAT YOU HAVE\n\n - 1–3 years in operations, people operations, recruiting coordination, office or business operations, ideally at a startup or another fast, scrappy team.\n\n - You finish things. Nothing you own falls through.\n\n - You're organized enough that a process you run is easy to audit and easy to hand off.\n\n - You write clearly and keep people informed without being asked.\n\n - You use AI tools in your own work every day and you're curious about doing more with them.\n\n - Discretion with personal and candidate information.\n\n - You're in New York City or willing to relocate.\n\n\nYOU MIGHT BE A FIT IF YOU ARE…\n\n - A recruiting coordinator who wants a bigger operations seat.\n\n - An operations or office lead at a small company who did a bit of everything.\n\n - An analyst or consultant who'd rather run things than recommend them.\n\n - Or you don't fit any of these neatly, but you've made a messy process run well. Apply.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: every new hire's first day is set up before they arrive, every interview loop has its feedback in on time, and the access list is current.\n\n - First 6 months: the recurring processes you run are all written down, and at least three of them have been automated with the engineer, on your recommendation.\n\n\nCOMPENSATION\n\n$95,000–$121,000 base salary, depending on experience. Equity. Medical, dental and vision.\n\n \n\n\nHOW TO APPLY\n\nSkip the cover letter. Tell us about a messy, recurring process you took over and made run better: what was broken, what you changed and what it looked like a month later. A few paragraphs.\n\n \n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"d6927c86-f616-427d-9d42-d1fd6c83c223","title":"Backend Engineer, Data","department":"Research & Development","team":"Engineering","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-23T17:01:34.129+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/d6927c86-f616-427d-9d42-d1fd6c83c223","applyUrl":"https://jobs.ashbyhq.com/minerva/d6927c86-f616-427d-9d42-d1fd6c83c223/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
A warehouse full of consumer data does nothing for a marketer until they can sort, filter and chart it in the time it takes to ask the question.
Our data team builds the consumer graph and each brand's first-party models, and our data scientists train scores on top. This seat makes all of it usable in the product: first-party fields that respond instantly, scores on the pages marketers already use, segments that stay current, and an agent that turns a plain-English question into the right query against a brand's own data.
This is an IC or senior IC seat on product engineering, working day to day with data engineering and data science. Your counterpart on the warehouse side is our Staff Data Engineer, who owns the golden model and publishes the semantic views you build on.
Own how first-party data performs in the app. Any field a brand connects can be sorted, filtered and charted without a live warehouse query. You decide what gets precomputed, where it lives and how it stays fresh.
Own how our models reach marketers. Propensity, LTV, churn and product-fit scores on the person, segment and product pages, with full distributions instead of a single number.
Own dynamic segments. Segments that update as new data lands, with stats ready for activation.
Own the in-app agent's query tool, built on the semantic views our data platform team publishes. You own the path from a marketer's question to the right rows.
Own the product side of the sync. What gets precomputed, and the path from Postgres to the synced client. Each hop costs money and time, and you choose on purpose.
Extend the pipelines. You mostly build on what the data team produces. When a feature needs a table that doesn't exist, you write the model or pipeline step yourself.
Ship end to end. One feature can mean a Python Lambda, a Zero schema change and a TanStack Start page. You ship all three.
App: TypeScript, TanStack Start, React 19, Tailwind and Base UI, on Vite and Bun.
Sync: Zero on Postgres, schema in Drizzle. The app reads from a live-synced local copy of the data instead of request-response endpoints.
AI: Mastra for agents, Temporal for long-running work.
Backend: Python on AWS Lambda.
Data: Snowflake, including semantic views and Cortex.
Production experience with a cloud data warehouse. Snowflake preferred.
Production TypeScript, and backend services shipped on AWS: Lambda or similar.
You're current. You know what shipped in AI tooling this month and what changed in the latest release of the packages you depend on.
You build things on your own time, and you can show us.
You're in New York City or willing to relocate.
A full-stack engineer at a data-heavy SaaS company who ended up owning the warehouse because someone had to, and liked it.
A product engineer at a warehouse-native analytics or activation company who has spent years making Snowflake feel fast inside an app.
A backend engineer who has shipped on a sync engine like Zero, Replicache or ElectricSQL, and wants a dataset big enough to stress it. We'd be especially impressed by this one.
A data engineer tired of handing tables to other teams who wants to own the feature the table ends up in.
Or you don't fit any of these neatly, but you built something this year that changed how you think data should reach a user. Apply.
First 90 days: new scores and first-party tables reach the app through a repeatable system you built.
First 6 months: any first-party field can be sorted, filtered and charted without a live warehouse query, and the in-app agent's query tool runs on your layer.
No single number. When product engineering has a data question, it comes to you.
$170,000–$236,000 base salary, depending on level. Meaningful equity. Medical, dental and vision.
Skip the cover letter. The application asks for a demo of something you built that's relevant to what we do at Minerva: a link, a repo or a two-minute video. Applications without a demo won't be reviewed.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nTHE ROLE\n\nA warehouse full of consumer data does nothing for a marketer until they can sort, filter and chart it in the time it takes to ask the question.\n\nOur data team builds the consumer graph and each brand's first-party models, and our data scientists train scores on top. This seat makes all of it usable in the product: first-party fields that respond instantly, scores on the pages marketers already use, segments that stay current, and an agent that turns a plain-English question into the right query against a brand's own data.\n\nThis is an IC or senior IC seat on product engineering, working day to day with data engineering and data science. Your counterpart on the warehouse side is our Staff Data Engineer, who owns the golden model and publishes the semantic views you build on.\n\n\nWHAT YOU'LL DO\n\n - Own how first-party data performs in the app. Any field a brand connects can be sorted, filtered and charted without a live warehouse query. You decide what gets precomputed, where it lives and how it stays fresh.\n\n - Own how our models reach marketers. Propensity, LTV, churn and product-fit scores on the person, segment and product pages, with full distributions instead of a single number.\n\n - Own dynamic segments. Segments that update as new data lands, with stats ready for activation.\n\n - Own the in-app agent's query tool, built on the semantic views our data platform team publishes. You own the path from a marketer's question to the right rows.\n\n - Own the product side of the sync. What gets precomputed, and the path from Postgres to the synced client. Each hop costs money and time, and you choose on purpose.\n\n - Extend the pipelines. You mostly build on what the data team produces. When a feature needs a table that doesn't exist, you write the model or pipeline step yourself.\n\n - Ship end to end. One feature can mean a Python Lambda, a Zero schema change and a TanStack Start page. You ship all three.\n\n\nOUR STACK\n\n - App: TypeScript, TanStack Start, React 19, Tailwind and Base UI, on Vite and Bun.\n\n - Sync: Zero on Postgres, schema in Drizzle. The app reads from a live-synced local copy of the data instead of request-response endpoints.\n\n - AI: Mastra for agents, Temporal for long-running work.\n\n - Backend: Python on AWS Lambda.\n\n - Data: Snowflake, including semantic views and Cortex.\n\n\nWHAT YOU HAVE\n\n - Production experience with a cloud data warehouse. Snowflake preferred.\n\n - Production TypeScript, and backend services shipped on AWS: Lambda or similar.\n\n - You're current. You know what shipped in AI tooling this month and what changed in the latest release of the packages you depend on.\n\n - You build things on your own time, and you can show us.\n\n - You're in New York City or willing to relocate.\n\n\nYOU MIGHT BE A FIT IF YOU ARE…\n\n - A full-stack engineer at a data-heavy SaaS company who ended up owning the warehouse because someone had to, and liked it.\n\n - A product engineer at a warehouse-native analytics or activation company who has spent years making Snowflake feel fast inside an app.\n\n - A backend engineer who has shipped on a sync engine like Zero, Replicache or ElectricSQL, and wants a dataset big enough to stress it. We'd be especially impressed by this one.\n\n - A data engineer tired of handing tables to other teams who wants to own the feature the table ends up in.\n\n - Or you don't fit any of these neatly, but you built something this year that changed how you think data should reach a user. Apply.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: new scores and first-party tables reach the app through a repeatable system you built.\n\n - First 6 months: any first-party field can be sorted, filtered and charted without a live warehouse query, and the in-app agent's query tool runs on your layer.\n\n - No single number. When product engineering has a data question, it comes to you.\n\n\nCOMPENSATION\n\n$170,000–$236,000 base salary, depending on level. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. The application asks for a demo of something you built that's relevant to what we do at Minerva: a link, a repo or a two-minute video. Applications without a demo won't be reviewed.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"85ca17d8-48ac-4e7a-ba45-bd307325fc82","title":"Executive Assistant","department":"Executive","team":"Executive","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-29T15:20:39.417+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/85ca17d8-48ac-4e7a-ba45-bd307325fc82","applyUrl":"https://jobs.ashbyhq.com/minerva/85ca17d8-48ac-4e7a-ba45-bd307325fc82/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We work with leading consumer brands across categories, including the NBA, Capital One, Hard Rock Stadium Group / Miami Dolphins, Wander and Trust & Will. We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
As our Executive Assistant, you’ll work primarily with Minerva’s CEO and support our two other co-founders as needed. Minerva builds AI agents that take operational work off marketing teams, and this role does the same for our founders: you’ll own their calendars, inboxes, travel and investor follow-ups, make judgment calls on their behalf, and use AI to keep getting faster. It’s a high-trust seat with a close view of how an early-stage AI company gets built.
You’ll report to our Chief of Staff and work from our beautiful Brooklyn office five days a week.
Own calendar management across all three founders, deciding what gets accepted, declined or moved when priorities compete.
Manage the founders’ inboxes, including triaging, drafting responses and flagging what needs their attention.
Coordinate complex travel arrangements, including flights, accommodations, and itineraries.
Keep investor follow-ups on track and on the calendar.
Adjust priorities dynamically in response to the fast-paced and evolving needs of a growing company
Plan events of every size, including team dinners, all-hands, company offsites and the logistics for board meetings.
Run the office and manage budgets, expenses and vendor relationships.
Use AI and automation to take repetitive work off your plate and the founders’.
Take on occasional personal tasks and errands so the founders can stay focused.
Experience as an Executive Assistant or in a comparable role, ideally supporting more than one executive
Experience in a start-up, high-growth, or entrepreneurial organization is highly desirable
Proven ability to manage complex calendars, shifting priorities, and multiple high-priority work streams
Strong organizational instincts and exceptional attention to detail. You anticipate what’s needed and ensure nothing falls through the cracks
A high level of discretion and professionalism with sensitive information
The energy to match founder intensity, including later hours when the work calls for it
Proficiency with modern productivity tools including Google Workspace, Slack, and Notion; ability to quickly adopt new and AI-powered workflows
Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Every new data source starts as someone else's problem: an API with rate limits, a vendor file that arrives late and gets corrected twice, a schema that changes without warning. Today, adding a new vendor feed still takes hand-built work.
You make new sources dependable and make the next one cheap. You build the ingestion pipelines for our customer and vendor data, automate onboarding from configuration to first delivery, and turn what you build into modules other Minerva teams can use without running platform infrastructure themselves.
This is a mid-level individual contributor seat on the data platform team, reporting to our Head of Data Platform. Our data product team decides which sources matter and what good data looks like. You own how it arrives and how it stays dependable.
Build pipelines for messy sources. APIs, files, database extracts and approved scraping sources, at high volume, with inconsistent formats and schemas that change.
Make reruns safe. Incremental loads, backfills and retries that never duplicate or corrupt data. Bad records get quarantined, not loaded. Throughput and processing cost stay under control.
Automate onboarding. The path from source configuration and access checks through provisioning, validation, deployment and first delivery runs without hand-holding.
Build modules other teams use. Reusable connectors and storage components with tests, examples and clear interfaces, so a team building an API integration or a scraper gets managed ingestion, storage and monitoring without building them.
Keep the origin of every value. Carry source metadata through the pipeline (which vendor, which delivery, when) so we can always say where a value came from. Enforce privacy controls so restricted data can't come back through a new load or a replay.
Own your integrations in production. Connect each one to shared monitoring, write its recovery runbook and fix source-specific problems when they break.
Orchestration: Dagster, with Airflow still running the workloads that haven't moved.
Transformation: dbt and SQLMesh.
Storage: Snowflake, plus a lakehouse on Spark, Iceberg, Trino and AWS Glue.
Code: Python on AWS.
Typically three or more years building and supporting production data pipelines, or equivalent ownership.
You've brought in data from many vendors and source types, including messy data at scale, and kept it running after launch.
Practical judgment about incremental processing, backfills, failures and cost. You can show the data stayed correct as volume grew.
You've automated repeated work or built components other engineers actually used.
Strong Python and SQL.
You pin down what a source owner actually needs, and you write down what you will and won't support.
You're in New York City or willing to relocate.
Connector frameworks, whether off the shelf or your own.
Onboarding automation or self-service data tooling.
Consumer, identity or contact data, and the privacy rules that come with it.
Dagster, Iceberg or Snowflake in production.
A data engineer at a data vendor or identity company who has onboarded source after source and wants to build the framework instead of the next one-off.
An engineer at a connector, integration or reverse-ETL company who has built connectors other people depend on.
A data engineer at a fintech, marketplace or adtech company who became the person every new integration ran through.
Or you don't fit any of these neatly, but you've taken data you didn't control and made it dependable. Apply.
First 90 days: a new source is live through automated onboarding you built, and another engineer has onboarded a different source with the same modules. You've measured the setup effort and tested recovery.
First 6 months: teams outside data platform bring in new sources through your modules, and every feed you own has alerting and a recovery runbook a second engineer has used.
The numbers you own: time to onboard a new source, and how often a feed needs hands-on recovery.
$170,000–$236,000 base salary, depending on level. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Walk us through one pipeline you built for data you didn't control: the source, what broke, how you made reruns safe and how the next source got easier. A few paragraphs. Applications without it won't be reviewed.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nTHE ROLE\n\nEvery new data source starts as someone else's problem: an API with rate limits, a vendor file that arrives late and gets corrected twice, a schema that changes without warning. Today, adding a new vendor feed still takes hand-built work.\n\nYou make new sources dependable and make the next one cheap. You build the ingestion pipelines for our customer and vendor data, automate onboarding from configuration to first delivery, and turn what you build into modules other Minerva teams can use without running platform infrastructure themselves.\n\nThis is a mid-level individual contributor seat on the data platform team, reporting to our Head of Data Platform. Our data product team decides which sources matter and what good data looks like. You own how it arrives and how it stays dependable.\n\n\nWHAT YOU'LL DO\n\n - Build pipelines for messy sources. APIs, files, database extracts and approved scraping sources, at high volume, with inconsistent formats and schemas that change.\n\n - Make reruns safe. Incremental loads, backfills and retries that never duplicate or corrupt data. Bad records get quarantined, not loaded. Throughput and processing cost stay under control.\n\n - Automate onboarding. The path from source configuration and access checks through provisioning, validation, deployment and first delivery runs without hand-holding.\n\n - Build modules other teams use. Reusable connectors and storage components with tests, examples and clear interfaces, so a team building an API integration or a scraper gets managed ingestion, storage and monitoring without building them.\n\n - Keep the origin of every value. Carry source metadata through the pipeline (which vendor, which delivery, when) so we can always say where a value came from. Enforce privacy controls so restricted data can't come back through a new load or a replay.\n\n - Own your integrations in production. Connect each one to shared monitoring, write its recovery runbook and fix source-specific problems when they break.\n\n\nOUR STACK\n\n - Orchestration: Dagster, with Airflow still running the workloads that haven't moved.\n\n - Transformation: dbt and SQLMesh.\n\n - Storage: Snowflake, plus a lakehouse on Spark, Iceberg, Trino and AWS Glue.\n\n - Code: Python on AWS.\n\n\nWHAT YOU HAVE\n\n - Typically three or more years building and supporting production data pipelines, or equivalent ownership.\n\n - You've brought in data from many vendors and source types, including messy data at scale, and kept it running after launch.\n\n - Practical judgment about incremental processing, backfills, failures and cost. You can show the data stayed correct as volume grew.\n\n - You've automated repeated work or built components other engineers actually used.\n\n - Strong Python and SQL.\n\n - You pin down what a source owner actually needs, and you write down what you will and won't support.\n\n - You're in New York City or willing to relocate.\n\n\nNICE TO HAVES\n\n - Connector frameworks, whether off the shelf or your own.\n\n - Onboarding automation or self-service data tooling.\n\n - Consumer, identity or contact data, and the privacy rules that come with it.\n\n - Dagster, Iceberg or Snowflake in production.\n\n\nYOU MIGHT BE A FIT IF YOU ARE…\n\n - A data engineer at a data vendor or identity company who has onboarded source after source and wants to build the framework instead of the next one-off.\n\n - An engineer at a connector, integration or reverse-ETL company who has built connectors other people depend on.\n\n - A data engineer at a fintech, marketplace or adtech company who became the person every new integration ran through.\n\n - Or you don't fit any of these neatly, but you've taken data you didn't control and made it dependable. Apply.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: a new source is live through automated onboarding you built, and another engineer has onboarded a different source with the same modules. You've measured the setup effort and tested recovery.\n\n - First 6 months: teams outside data platform bring in new sources through your modules, and every feed you own has alerting and a recovery runbook a second engineer has used.\n\n - The numbers you own: time to onboard a new source, and how often a feed needs hands-on recovery.\n\n\nCOMPENSATION\n\n$170,000–$236,000 base salary, depending on level. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Walk us through one pipeline you built for data you didn't control: the source, what broke, how you made reruns safe and how the next source got easier. A few paragraphs. Applications without it won't be reviewed.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."},{"id":"fe456931-ec73-4566-8b35-ee43e67e0e97","title":"Founding Customer Success Engineer","department":"Customer","team":"Customer Success","employmentType":"FullTime","location":"New York City","secondaryLocations":[],"publishedAt":"2026-10-24T04:12:00.258+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York City"}},"jobUrl":"https://jobs.ashbyhq.com/minerva/fe456931-ec73-4566-8b35-ee43e67e0e97","applyUrl":"https://jobs.ashbyhq.com/minerva/fe456931-ec73-4566-8b35-ee43e67e0e97/application","descriptionHtml":"Minerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.
Customers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.
We've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.
Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.
Customers also use Minerva directly, in the platform and through our API. Those customers, and every question that repeats across accounts, belong to customer success.
Today, when a customer gets stuck, the answer comes from whoever built the feature or whoever is on the account. It's good help, but it's one at a time, it isn't written down, and the same question comes back next week.
You're Minerva's first customer success engineer. Your job is to make sure every customer can get value out of Minerva without waiting on a person, and that no question gets answered twice. You run the support queue, diagnose what comes in, and turn every answer worth keeping into something customers can find on their own. Then you take the patterns to product, so the friction gets fixed at the source.
It's a founding seat. Nobody reports to you on day one, and there's no playbook yet. You'll work most closely with our product and engineering team.
Run the support queue. You own Pylon and our shared customer channels: triage, response times and the first read on everything that comes in.
Diagnose, don't forward. Reproduce the issue, read the data, and either solve it or hand engineering a real diagnosis with a reproduction attached.
Build the knowledge base from zero. Every repeat question becomes an article, a how-to or a short video: how the features and limits work, how to use the API, how to build an audience or write a campaign brief. You keep it accurate as the platform changes.
Make self-serve work. Own the path a new customer takes from first login to first result, and keep shortening it.
Turn friction into product signal. Bring product the questions customers keep asking, ranked by how often they come up, so the fix lands in the platform and not just the docs.
Write the playbook. Escalation paths, response standards and self-serve onboarding checklists: the function a future team will run.
3+ years in customer success engineering, technical support, solutions or a technical account role, ideally at a data, martech, adtech or developer-tools company.
SQL, and comfort with APIs. You can read a schema and work out why a result came back wrong. This is the floor.
You diagnose instead of escalating. You have a real story about a hard problem you chased down yourself.
You write clearly. You've built documentation or a knowledge base that people actually used.
You'd rather fix something once for every customer than a hundred times for one.
You're in New York City or willing to relocate.
Technical documentation you wrote that's publicly live. Link it.
You've run support on Pylon, Intercom, Zendesk or something like them.
Identity resolution, CDPs, match rates or marketing data.
You've made short product explainer videos.
Account management. Our account managers own named accounts, renewals and expansion. You make sure they rarely have to answer a how-to question.
Implementation. Our solutions engineers and forward deployed engineers build customer pipelines, and you know when to hand a problem to them.
Marketing. Our marketing team writes for people deciding whether to buy. You write for the customers who already did.
First 90 days: Pylon is a real queue with response-time standards, the knowledge base covers the questions that come up most, and engineering gets diagnoses from you, not forwards.
First 6 months: repeat questions are falling, more issues get solved without engineering, and new self-serve customers reach a first result faster. A few roadmap items exist because you brought product a pattern with numbers attached.
The numbers you own: the share of questions resolved without engineering, the repeat-question rate, and time to first result for self-serve customers.
$149,000–$189,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.
Skip the cover letter. Tell us about a customer problem you diagnosed where the obvious answer was wrong: the symptom, what you checked, what it actually was, and what you wrote down afterward so it wouldn't happen again. A few paragraphs. If you've published documentation, link it.
We work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn.
","descriptionPlain":"ABOUT MINERVA\n\nMinerva builds AI for marketing leaders. We work together with marketers to automate and accelerate their workflows with AI agents. Our agents augment the end-to-end workflow of the marketer by automating analysis, campaign planning, activation, measurement and reporting.\n\nCustomers like the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence use Minerva to develop a better understanding of their customers and introduce AI specifically built to understand the customer into their commercial organizations. Our campaigns outperform other tools because we built the entire stack: from identity to enrichment to data integration as well as agents to action and report on the analysis.\n\nWe've raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and many others. Our team brings together operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, alongside engineers and researchers from Berkeley, MIT, Stanford and Cambridge.\n\n\nHOW WE WORK WITH CUSTOMERS\n\n - Every enterprise account has a team. The account executive owns the deal, and the account manager owns the relationship and the renewal after it. A forward deployed marketer owns the pilot's outcome. A solutions engineer runs the technical evaluation and gets standard accounts live on our product. A forward deployed engineer builds what our product can't do yet.\n\n - Customers also use Minerva directly, in the platform and through our API. Those customers, and every question that repeats across accounts, belong to customer success.\n\n\nTHE ROLE\n\nToday, when a customer gets stuck, the answer comes from whoever built the feature or whoever is on the account. It's good help, but it's one at a time, it isn't written down, and the same question comes back next week.\n\nYou're Minerva's first customer success engineer. Your job is to make sure every customer can get value out of Minerva without waiting on a person, and that no question gets answered twice. You run the support queue, diagnose what comes in, and turn every answer worth keeping into something customers can find on their own. Then you take the patterns to product, so the friction gets fixed at the source.\n\nIt's a founding seat. Nobody reports to you on day one, and there's no playbook yet. You'll work most closely with our product and engineering team.\n\n\nWHAT YOU'LL DO\n\n - Run the support queue. You own Pylon and our shared customer channels: triage, response times and the first read on everything that comes in.\n\n - Diagnose, don't forward. Reproduce the issue, read the data, and either solve it or hand engineering a real diagnosis with a reproduction attached.\n\n - Build the knowledge base from zero. Every repeat question becomes an article, a how-to or a short video: how the features and limits work, how to use the API, how to build an audience or write a campaign brief. You keep it accurate as the platform changes.\n\n - Make self-serve work. Own the path a new customer takes from first login to first result, and keep shortening it.\n\n - Turn friction into product signal. Bring product the questions customers keep asking, ranked by how often they come up, so the fix lands in the platform and not just the docs.\n\n - Write the playbook. Escalation paths, response standards and self-serve onboarding checklists: the function a future team will run.\n\n\nWHAT YOU HAVE\n\n - 3+ years in customer success engineering, technical support, solutions or a technical account role, ideally at a data, martech, adtech or developer-tools company.\n\n - SQL, and comfort with APIs. You can read a schema and work out why a result came back wrong. This is the floor.\n\n - You diagnose instead of escalating. You have a real story about a hard problem you chased down yourself.\n\n - You write clearly. You've built documentation or a knowledge base that people actually used.\n\n - You'd rather fix something once for every customer than a hundred times for one.\n\n - You're in New York City or willing to relocate.\n\n\nNICE TO HAVES\n\n - Technical documentation you wrote that's publicly live. Link it.\n\n - You've run support on Pylon, Intercom, Zendesk or something like them.\n\n - Identity resolution, CDPs, match rates or marketing data.\n\n - You've made short product explainer videos.\n\n\nWHAT THIS ISN'T\n\n - Account management. Our account managers own named accounts, renewals and expansion. You make sure they rarely have to answer a how-to question.\n\n - Implementation. Our solutions engineers and forward deployed engineers build customer pipelines, and you know when to hand a problem to them.\n\n - Marketing. Our marketing team writes for people deciding whether to buy. You write for the customers who already did.\n\n\nWHAT SUCCESS LOOKS LIKE\n\n - First 90 days: Pylon is a real queue with response-time standards, the knowledge base covers the questions that come up most, and engineering gets diagnoses from you, not forwards.\n\n - First 6 months: repeat questions are falling, more issues get solved without engineering, and new self-serve customers reach a first result faster. A few roadmap items exist because you brought product a pattern with numbers attached.\n\n - The numbers you own: the share of questions resolved without engineering, the repeat-question rate, and time to first result for self-serve customers.\n\n\nCOMPENSATION\n\n$149,000–$189,000 base salary, depending on experience. Meaningful equity. Medical, dental and vision.\n\n\nHOW TO APPLY\n\nSkip the cover letter. Tell us about a customer problem you diagnosed where the obvious answer was wrong: the symptom, what you checked, what it actually was, and what you wrote down afterward so it wouldn't happen again. A few paragraphs. If you've published documentation, link it.\n\n\nWHERE WE WORK\n\nWe work in person from our office at 300 Kent Avenue in Williamsburg, Brooklyn."}],"apiVersion":"1"}