About LanceDB
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
About the Role
Takes high-level direction (e.g., “analyze X”) and drives to results across the company
Success looks like a LanceDB platform that is even more tightly focused on our ICP, and providing clarity on what engineering builds
What You'll Do
Shape roadmap with CTO + eng leadership
Deep dives into competitors, users, and adjacent tech
Talk to users constantly; turn insight into product changes
Drive cross-team decisions on priorities (eng, field, support)
Work with engineering directly as a technically-respected peer
What We're Looking For
10+ years in a highly technical role
5+ years of product work
Shipped and iterated on products end-to-end
Experience working on large-scale data platforms (e.g., databases, data infra, ML/AI systems)
Understanding of AI researcher persona - what success looks like for them, what are their pain points, what increases their happiness
Regularly engaged with users (interviews, support, design partners, or community)
Demonstrated ownership of ambiguous projects with minimal guidance
Experience influencing or driving roadmap / prioritization decisions
Strong written and verbal communication (can drive alignment across teams)
Comfortable using data (metrics, experiments, usage) to guide decisions
Experience contributing to or working with open source communities
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
We're looking for a hands-on, technically strong Support Engineer who will be the bridge between our engineering team and enterprise users of LanceDB, helping our customers debug distributed databases built in Rust.
As one of the early team members, build our support infrastructure and practices while handling customer cases:
Develop and maintain knowledge-base articles, runbooks, and support tooling that document common issues, best practices, deployment patterns, and performance tuning.
Contribute to metrics around support response-times, resolution times, customer satisfaction, and help build a scalable support organization as we grow.
Work proactively: identify recurring issues, escalate product bugs or UX gaps, propose improvements in the support process, and advocate for the customer in the roadmap.
Serve as one of the primary technical points of contact for our customers: troubleshoot issues, respond to escalations, and guide customers through full lifecycle support for large-scale deployments of LanceDB.
Work in close collaboration with our engineering and product teams to reproduce issues, debug root causes, propose remediation, and drive fixes or enhancements.
Dive deeply into distributed database internals: query execution, storage engine, indexing, sharding, replication, fail-over, and cloud orchestration (Kubernetes, serverless-style deployments).
Use and contribute to Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes.
Please do not apply unless you meet all of the criteria in this section.
8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment.
Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.
Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).
Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.
Very comfortable with reading logs and correlating them with source code, working with Grafana dashboards, and creating shell scripts or Python code to assist in debugging.
Excellent customer-facing communication skills: you'll be working directly with high-value customers, so you must be comfortable explaining complex technical issues clearly, managing expectations, and advocating for the customer.
Strong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution.
Comfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities.
Proficiency in Rust: you should be comfortable reading, navigating, and debugging code; ideally you've built or debugged production-quality systems written in Rust.
Familiarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools.
Contributions to open-source projects (especially Rust), or experience writing diagnostic tools, debuggers, or instrumentation.
Experience deploying and monitoring systems in large-scale production environments: logging/observability (e.g., Prometheus, Grafana, OpenTelemetry), alerting, SLOs/SLAs.
Previous experience creating new runbooks, selecting and configuring support ticketing systems, and defining incident response processes.
LanceDB is a developer-friendly, open-source data lake for multimodal AI. From hyper-scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application, and powers some of the most groundbreaking applications and challenging requirements today.
We are looking for a Senior Solutions Engineer who blends deep technical understanding of AI/ML infrastructure with excellent communication and solution-building skills. In this role, you will serve as a trusted advisor to prospective customers, design partners, and strategic accounts—bridging the gap between cutting-edge AI engineering and real-world business use cases.
This role is ideal for someone who thrives at the intersection of technical depth and customer interaction, and who enjoys crafting solutions, demos, and integrations that showcase LanceDB’s strengths in production environments.
Serve as the technical lead in pre-sales conversations—partnering with account executives to scope, solution, and articulate the value of LanceDB for customer-specific workflows.
Lead technical discovery and architecture design sessions with prospects across verticals including AI infra, LLM ops, and multimodal data pipelines.
Build and deliver custom demos and proof-of-concepts to highlight how LanceDB solves challenging RAG, vector search, and feature engineering problems.
Act as the bridge between customer pain points and our engineering/product teams—informing roadmap priorities with real-world feedback.
Partner closely with design partners and early adopters to ensure successful onboarding and expansion.
Champion a superior developer experience with a sharp focus on documentation, SDK ergonomics, and integration workflows.
You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams.
You have 5+ years of experience in a Sales Engineer, Solutions Engineer, ML Engineer, or AI Infrastructure role, supporting AI/ML products or platforms.
Strong knowledge of AI/ML frameworks like PyTorch or TensorFlow, and how they integrate with infrastructure for model training, fine-tuning, and inference.
Hands-on experience working with distributed systems such as Ray, Spark, or Kubernetes.
Familiarity with cloud services (AWS, GCP, Azure) including compute and storage (e.g., EC2, GKE, S3).
Confident communicating with both technical and non-technical stakeholders, and able to translate complex infrastructure into actionable solutions.
You must be based out of the San Francisco Bay Area, and be willing to travel to customer sites as needed. This position is only available to candidates that fulfill this criteria.
Have experience building or supporting feature engineering workflows or vector search pipelines.
Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines.
Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK).
Are familiar with open-source data/streaming frameworks such as Apache Spark, Flink, Delta Lake, Kafka, or Airflow.
Have deep Python skills or are curious about Rust.
Are comfortable creating technical content, workshops, or presenting at meetups/conferences.
Have experience deploying ML infrastructure in customer environments using tools like Terraform, Docker, and CI/CD pipelines.
Have supported enterprise customers or worked in a customer-facing technical capacity before.
You’ll join a world-class team of open-source builders working on cutting-edge AI infrastructure. You’ll collaborate on systems that power next-generation AI workloads while shaping how LanceDB operates and scales production environments.
","descriptionPlain":"ABOUT LANCEDB\n\nLanceDB http://lancedb.com/ is a developer-friendly, open-source data lake for multimodal AI. From hyper-scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application, and powers some of the most groundbreaking applications and challenging requirements today.\n\n\nABOUT THE ROLE\n\nWe are looking for a Senior Solutions Engineer who blends deep technical understanding of AI/ML infrastructure with excellent communication and solution-building skills. In this role, you will serve as a trusted advisor to prospective customers, design partners, and strategic accounts—bridging the gap between cutting-edge AI engineering and real-world business use cases.\n\nThis role is ideal for someone who thrives at the intersection of technical depth and customer interaction, and who enjoys crafting solutions, demos, and integrations that showcase LanceDB’s strengths in production environments.\n\n\nYOUR RESPONSIBILITIES WILL INCLUDE\n\n - Serve as the technical lead in pre-sales conversations—partnering with account executives to scope, solution, and articulate the value of LanceDB for customer-specific workflows.\n\n - Lead technical discovery and architecture design sessions with prospects across verticals including AI infra, LLM ops, and multimodal data pipelines.\n\n - Build and deliver custom demos and proof-of-concepts to highlight how LanceDB solves challenging RAG, vector search, and feature engineering problems.\n\n - Act as the bridge between customer pain points and our engineering/product teams—informing roadmap priorities with real-world feedback.\n\n - Partner closely with design partners and early adopters to ensure successful onboarding and expansion.\n\n - Champion a superior developer experience with a sharp focus on documentation, SDK ergonomics, and integration workflows.\n\n\nREQUIREMENTS\n\n - You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams.\n\n - You have 5+ years of experience in a Sales Engineer, Solutions Engineer, ML Engineer, or AI Infrastructure role, supporting AI/ML products or platforms.\n\n - Strong knowledge of AI/ML frameworks like PyTorch or TensorFlow, and how they integrate with infrastructure for model training, fine-tuning, and inference.\n\n - Hands-on experience working with distributed systems such as Ray, Spark, or Kubernetes.\n\n - Familiarity with cloud services (AWS, GCP, Azure) including compute and storage (e.g., EC2, GKE, S3).\n\n - Confident communicating with both technical and non-technical stakeholders, and able to translate complex infrastructure into actionable solutions.\n\n - You must be based out of the San Francisco Bay Area, and be willing to travel to customer sites as needed. This position is only available to candidates that fulfill this criteria.\n\n\nBONUS POINTS IF YOU\n\n - Have experience building or supporting feature engineering workflows or vector search pipelines.\n\n - Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines.\n\n - Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK).\n\n - Are familiar with open-source data/streaming frameworks such as Apache Spark, Flink, Delta Lake, Kafka, or Airflow.\n\n - Have deep Python skills or are curious about Rust.\n\n - Are comfortable creating technical content, workshops, or presenting at meetups/conferences.\n\n - Have experience deploying ML infrastructure in customer environments using tools like Terraform, Docker, and CI/CD pipelines.\n\n - Have supported enterprise customers or worked in a customer-facing technical capacity before.\n\n\nWHY JOIN US\n\nYou’ll join a world-class team of open-source builders working on cutting-edge AI infrastructure. You’ll collaborate on systems that power next-generation AI workloads while shaping how LanceDB operates and scales production environments."},{"id":"13c553bc-a55b-40b5-b3c9-ba01024afc6c","title":"Partner Solutions Architect","department":"Field Engineering","team":"Field Engineering","employmentType":"FullTime","location":"United States | Canada","secondaryLocations":[],"publishedAt":"2026-07-10T15:39:37.832+00:00","isListed":true,"isRemote":null,"workplaceType":null,"address":null,"jobUrl":"https://jobs.ashbyhq.com/lancedb/13c553bc-a55b-40b5-b3c9-ba01024afc6c","applyUrl":"https://jobs.ashbyhq.com/lancedb/13c553bc-a55b-40b5-b3c9-ba01024afc6c/application","descriptionHtml":"AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As a Senior Partner Solutions Architect (PSA), you will sit at the intersection of product engineering, business development, and customer success. You will be the trusted technical advisor to LanceDB's strategic partner ecosystem, including global System Integrators (SIs), Cloud Service Providers (AWS, GCP, Azure), Technology Alliances, and AI/ML orchestration platforms.
Your mission is to enable, empower, and unblock our partners so they can successfully architect, deploy, and scale LanceDB solutions on behalf of their enterprise customers. This role requires a unique blend of deep technical grit (distributed systems, AI/ML pipelines) and exceptional relationship-building skills to drive mutual growth and technical excellence.
Partner Enablement & Training: Lead technical onboarding, training programs, and certifications for partner engineers and architects. Equip them to independently deliver and support LanceDB implementations.
Joint Architecture & Co-Delivery: Partner with alliances and field teams to support high-value proofs-of-concept (PoCs), design reviews, and architectural validations for complex, cloud-native enterprise environments.
Scalable Technical Content: Author and maintain partner-facing technical assets, including production-ready reference architectures, integration guides, deployment blueprints (Terraform, Docker), and sample code.
Product & Ecosystem Advocate: Act as the primary technical liaison between our partners and LanceDB's internal Product and Engineering teams. Synthesize partner-sourced feedback and feature requests to directly influence our roadmap.
Go-To-Market (GTM) Collaboration: Participate in joint business planning and support regional technical marketing events, hackathons, and campaigns alongside channel account managers.
Thought Leadership: Drive ecosystem adoption by sharing best practices through technical blogs, whitepapers, open-source contributions, and presentations at major industry conferences (e.g., AWS re:Invent, AI meetups).
Experience: 10+ years of professional experience in customer- or partner-facing technical roles (e.g., Solutions Architecture, Partner Engineering, Sales Engineering, or ML Infrastructure), ideally supporting data platforms or distributed systems.
Ecosystem Familiarity: Proven track record working with or within a partner ecosystem (SIs, cloud providers, or technology alliances) with a firm grasp of how partners take solutions to market.
Technical Depth: Deep understanding of distributed systems concepts (sharding, replication, partitioning, and performance tuning) and container orchestration (Kubernetes, cloud object storage).
Programming Skills: Strong proficiency in Python and a willingness to dive into Rust (or vice versa) to read, debug, and write production-grade integration code or SDK extensions.
Communication: Exceptional presentation and communication skills. You can translate complex data infrastructure into actionable solutions for audiences ranging from partner developers to C-level executives.
Startup Agility: A self-starter mindset with the ability to thrive and operate autonomously in fast-moving, ambiguous environments.
Hands-on experience building or supporting vector search pipelines, RAG applications, feature stores, or multimodal AI architectures.
Experience with open-source data frameworks and infrastructure orchestration tools (e.g., Apache Spark, Ray, Delta Lake, Terraform, Kafka, or Airflow).
Familiarity with modern observability and monitoring stacks (Prometheus, Grafana, OpenTelemetry) for troubleshooting distributed workloads.
Active contributions to open-source communities or a portfolio of developer-facing technical content (blogs, tutorials, GitHub repositories).
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
Our first product security-focused engineer
Takes high-level direction (e.g., “identify top five security-related gaps for AX”) and drives to results
Success looks like a LanceDB platform that follows security-related best practices, and an ongoing partnership with other engineers to continuously enhance our security posture in all areas of the product
Drive new security-related functionality such as key rotation, user-managed API credentials, RBAC, and the like
Select, deploy and tune security tooling across all relevant repos and environments, ensuring full coverage
Apply relevant industry trends, best practices, and specific vulnerabilities to our product
8+ years as a software engineer, with a significant portion of that time working on appsec-related work
Experience working on large-scale data platforms (e.g., databases, data infra, ML/AI systems), including work on concurrency and multitenancy
Demonstrated ability to code in Rust and/or C++
Experience building encryption, authentication, and/or authorization features for shipping products at high throughput
Previous experience working at an early-stage startup
Demonstrated ownership of ambiguous projects with minimal guidance
Strong written and verbal communication (can drive alignment across teams)
Comfortable using data (metrics, experiments, usage) to guide decisions
Experience contributing to or working with open source communities
Please apply for this role only if you meet all of the above qualifications, and agree with the day-to-day responsibilities. Of particular note: qualified candidates for this role will have day-to-day coding responsibilities - this is an appsec and devsecops role, not an infosec or platform security role.
","descriptionPlain":"ABOUT LANCEDB\n\nAI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.\n\n\nABOUT THE ROLE\n\n - Our first product security-focused engineer\n\n - Takes high-level direction (e.g., “identify top five security-related gaps for AX”) and drives to results\n\n - Success looks like a LanceDB platform that follows security-related best practices, and an ongoing partnership with other engineers to continuously enhance our security posture in all areas of the product\n\n\nWHAT YOU’LL DO\n\n - Drive new security-related functionality such as key rotation, user-managed API credentials, RBAC, and the like\n\n - Select, deploy and tune security tooling across all relevant repos and environments, ensuring full coverage\n\n - Apply relevant industry trends, best practices, and specific vulnerabilities to our product\n\n\nWHAT WE’RE LOOKING FOR\n\n - 8+ years as a software engineer, with a significant portion of that time working on appsec-related work\n\n - Experience working on large-scale data platforms (e.g., databases, data infra, ML/AI systems), including work on concurrency and multitenancy\n\n - Demonstrated ability to code in Rust and/or C++\n\n - Experience building encryption, authentication, and/or authorization features for shipping products at high throughput\n\n - Previous experience working at an early-stage startup\n\n - Demonstrated ownership of ambiguous projects with minimal guidance\n\n - Strong written and verbal communication (can drive alignment across teams)\n\n - Comfortable using data (metrics, experiments, usage) to guide decisions\n\n - Experience contributing to or working with open source communities\n\nPlease apply for this role only if you meet all of the above qualifications, and agree with the day-to-day responsibilities. Of particular note: qualified candidates for this role will have day-to-day coding responsibilities - this is an appsec and devsecops role, not an infosec or platform security role.\n\n"},{"id":"c2cebd35-0ac0-4f2c-ad3a-36798c200432","title":"Founding Revenue Marketer","department":"Go-to-Market","team":"Go-to-Market","employmentType":"FullTime","location":"United States | Canada","secondaryLocations":[],"publishedAt":"2026-08-18T20:51:07.289+00:00","isListed":true,"isRemote":null,"workplaceType":null,"address":null,"jobUrl":"https://jobs.ashbyhq.com/lancedb/c2cebd35-0ac0-4f2c-ad3a-36798c200432","applyUrl":"https://jobs.ashbyhq.com/lancedb/c2cebd35-0ac0-4f2c-ad3a-36798c200432/application","descriptionHtml":"AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
We're looking for our first Revenue Marketer to build the demand engine for LanceDB from the ground up. This is a hands-on, founding role: you'll own the marketing that turns LanceDB's technical credibility and open-source momentum into an enterprise pipeline, with account-based marketing as your core motion.
You'll partner directly with the founders and our early sales team to define our account-based marketing motion and how Marketing will support maximum revenue generation by Sales. This role is ideal for a builder who's energized by ambiguity and a blank slate: think someone equally happy designing a 1:1 ABM play, standing up the martech stack, and instrumenting pipeline reporting as they are shaping GTM strategy. You won't inherit a playbook, a team, or a tech stack. It's a rare opportunity for a marketer who’s enticed by the chance to build and realize something new.
Build and own the revenue marketing function end to end, including strategy, campaigns, content, and reporting, with 1:1, 1:few, and 1:many account-based marketing as the core motion.
Build the segmentation, prioritization, and marketing motion around our core ICP and target account list. Working closely with RevOps and Sales, ensure your marketing efforts reach the accounts that matter the most.
Design and bring to life custom, omnichannel marketing plans for key accounts with hyper-relevant, tailored experiences, content, and programs to nurture net-new opportunities and expand within existing accounts.
Manage a regular account-planning interlock with Sales and RevOps—reviewing each priority account, diagnosing where deals are stuck, and identifying next steps for where Marketing can help.
Build assets for ABM campaigns independently (landing pages, email sequences, LinkedIn creative, etc.). This is a role for a doer, not just an organizer.
Tap our open-source community and product-usage signal (GitHub, docs, self-serve) to surface high-intent accounts not otherwise targeted by our programs.
Work with Revenue Operations to stand up the marketing tech stack (CRM data hygiene with sales, ABM/attribution tooling, advertising, email, analytics) and the reporting that proves impact.
Work with our Partners team to drive co-marketing initiatives that amplify reach and increase account penetration.
You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams.
7-10 years of experience in B2B marketing for technical products, with real depth in ABM and revenue marketing.
Track record of sourcing and influencing enterprise pipeline through account-based campaigns.
Hands-on experience building or scaling marketing motions at an early-stage or high-growth startup. You have taste and strong pattern-matching for common problems and will move quickly and with confidence.
Proven ability to own campaign execution end-to-end without needing a full team to support you.
Proficiency in the full martech and ABM stack, including tools for CRM/automation (e.g. HubSpot), signals (e.g. Common Room), workbenches for enrichment and automation (e.g. Clay), digital and physical advertising, OSS usage intelligence, and customer journey intelligence.
Strong analytic instincts, with the ability to use data to draw conclusions and adjust as needed.
You use AI thoughtfully as a marketer, iterating on how it can improve and streamline your work.
A foundation-builder's mindset: you're motivated by creating process, content, and measurement where little exists today.
Excellent communicator who can partner with founders and GTM leadership and move fast in a small, high-ownership team.
Have experience in AI companies, databases and data infrastructure, developer tools, or other products for technical practitioners.
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As the Controller at LanceDB, you will own the company’s accounting function and build the financial infrastructure needed to support the next stage of growth.
You'll partner closely with the Leadership team, GTM and Engineering to deliver a reliable financial close, maintain strong financial controls and compliance, and provide leaders with clear and timely financial information.
This is an opportunity to be the first Controller at a fast-growing company and build a critical function from the ground up. You’ll have significant ownership over the accounting function and the opportunity to establish the processes, systems and team that will support LanceDB through its next phases of growth.
Own accounting operations: Lead day-to-day accounting operations, including accounts payable, accounts receivable, payroll accounting, fixed assets, accruals, prepaids, and general ledger management.
Lead financial close and reporting: Own the monthly, quarterly, and annual financial close process, including preparation of financial statements and supporting schedules, to deliver accurate and timely financial results for the company.
Own financial systems and infrastructure: Own and continuously improve the company's financial systems and accounting technology stack, including the ERP and related integrations, with a focus on scalability, accuracy, automation, and efficiency.
Manage tax and compliance: Oversee corporate income tax, sales/use tax, payroll tax, and other applicable federal, state, and local tax and licensing requirements in partnership with external advisors.
Scale the accounting function: Prepare the company for increasing financial complexity, including future fundraising, audit requirements, and potential international expansion, by establishing scalable processes, controls, and reporting infrastructure.
Partner with leadership: Serve as a trusted financial partner to the CFO, executives, and cross-functional leaders, translating accounting and financial issues into clear business implications and actionable recommendations.
Build and lead the accounting team: Attract, hire, develop, and retain the accounting team as the company scales. Manage our outsourced accounting firm and serve as a hands-on leader and mentor to the team.
8+ years of progressive accounting experience, ideally at a high-growth technology or SaaS company.
Proven track record of building accounting functions, systems, processes, and controls from the ground up and scale them as the business grows.
Strong technical accounting foundation and deep understanding of US GAAP.
Excellent analytical, organizational, communication and problem-solving skills.
Ability to operate effectively in a fast-paced, ambiguous and highly collaborative environment.
Strong business judgment and the ability to balance strategic thinking with hands-on execution.
Experience partnering cross-functionally with Legal, People, Operations and executive leadership.
Experience hiring, developing and managing an accounting team.
Passion for building strong foundations and relationships that enable a company to scale.
CPA or equivalent accounting qualification.
Experience at a venture-backed, Series B or later technology company.
Experience implementing an ERP and broader finance technology stack.
Experience with revenue recognition for subscription or usage-based business models.
Experience supporting investor relations.
Experience working with lean, distributed and remote-first teams.
About LanceDB
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
About the Role
We're looking for a Senior Software Engineer to help expand the reach of Lance and LanceDB within the broader data infrastructure ecosystem. You'll work at the intersection of high-performance computing, big data, and open-source systems. You will contribute scale and performance improvements, integrations with the wider data and AI ecosystem, simplifying distributed operations, and usability and maintainability enhancements.
What You'll Do
Designing and maintaining efficient distributed Lance dataset operations
Building efficient indices to enable predicate pushdown and accelerate queries in Spark, Ray, or Trino
Working on table formats, data encodings, and various aspects of the Lance format in Rust
Driving open-source community efforts to integrate the Lance format with Spark, Hive Metastore, Presto, Trino, Ray, and other data infrastructure systems
Operating and improving internal data processing infrastructure
Promoting the Lance format in open-source communities and at Big Data conferences
What We're Looking For
10+ years of experience building high-performance databases, big data systems, or large-scale data services
Deep understanding of internals of open-source Big Data or AI training systems (e.g., Hadoop, Spark, Flink, Ray, Iceberg, Delta Lake, Hudi, ClickHouse, Trino, Presto, PyTorch, or JAX)
Strong experience with high-performance computing in C++, Java, and/or Scala
Experience with Rust (or willingness to learn it)
Proven ability to move fast, work independently, and collaborate with a high-caliber team
Nice to Have
Contributor, committer, or PMC member in Apache or other large open-source projects
Experience with Apache Arrow, DataFusion, Parquet, Iceberg, or Delta Lake
Track record of driving large features or integrations in distributed systems
Strong community presence and passion for open-source collaboration
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As the Senior Customer Success Engineer, you will be a trusted advisor to our most strategic customers. You'll combine deep technical expertise with outstanding communication and relationship-building skills to ensure customers achieve success in deploying and scaling LanceDB across production workloads.
You'll guide customers from onboarding through adoption and expansion — while flexing seamlessly into pre-sales or technical support capacities as business needs require. You'll serve as the connective tissue between our customers, product, and engineering teams, driving continuous improvement in both the customer experience and the product itself.
Partner closely with customers to design, deploy, and optimize LanceDB in production environments, ensuring reliability, scalability, and performance for distributed, cloud-native workloads.
Lead technical onboarding and architecture reviews; provide best-practice guidance on system configuration, query optimization, and integration patterns.
Proactively identify adoption barriers, troubleshoot complex distributed-system issues, and coordinate with product and engineering teams to drive timely resolutions.
Own customer success metrics: deployment time, usage growth, retention, and satisfaction. Build dashboards and track health across accounts.
Develop and deliver technical enablement: create sample code, automation tools, and documentation to accelerate customer outcomes.
Serve as the customer's technical advocate internally — communicating feature requests, influencing roadmap priorities, and improving developer experience.
Collaborate cross-functionally with sales engineering (for technical evaluations, proofs-of-concept, and demos) and support engineering (for escalations and issue triage).
Contribute to internal tooling, runbooks, and playbooks that will form the foundation of LanceDB's future customer success organization.
Help to shape processes, tooling, and team culture as we scale customer success and post-sales engineering.
10+ years of professional experience in technical roles such as post-sales engineering, customer success, solutions architecture, or technical support, ideally within the data infrastructure or distributed systems space.
Proven track record supporting or deploying distributed database systems or large-scale cloud-native data platforms (e.g., high-availability, multi-region, and horizontally scalable environments).
Strong proficiency in Rust and Python — able to read, debug, and write production-grade code in both languages.
Deep understanding of distributed systems concepts: sharding, replication, consensus, partitioning, failure recovery, and performance tuning.
Experience deploying and managing workloads on Kubernetes or other container orchestration frameworks, and familiarity with cloud environments (AWS, GCP, Azure).
Exceptional communication and presentation skills: able to engage directly with customers' engineering leaders, architects, and executives with credibility and empathy.
Strong problem-solving ability, coupled with a customer-first mindset and the ability to operate autonomously in fast-moving, ambiguous environments.
Willingness and ability to flex across functions — including pre-sales engineering, technical support, and post-sales enablement — as needed by the business.
Previous experience as a founding or early member of a customer success or solutions engineering function at a high-growth startup.
Hands-on experience with vector search, feature stores, or AI-native data systems.
Contributions to open-source projects (especially in Rust or Python) or experience authoring developer-facing technical content.
Familiarity with modern observability stacks (Prometheus, Grafana, OpenTelemetry) and incident management best practices.
Experience designing or leading enterprise architecture workshops or technical proof-of-concepts.
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As the AI research engineer at LanceDB, you'll work with the research team to perform fairly open ended research, focused on end to end training flows across different AI domains, showcasing how LanceDB can be used to accelerate research flows.
This is an opportunity to pursue your research interest as an engineer, and have a meaningful impact on raising awareness and significantly improve the product.
Show how Lancedb can be used for training models end to end from curation to modeling across industry verticals
Compare the Lancedb stacked workflow with existing standard training flows with well designed and replicable experiments, that may include benchmarking
Provide core content and work cross-function with to increase awareness for workflow specific features like blobv2, distributed indexing etc.
Publish models and research papers on LanceDB blog platform, social media, and in AI conferences
Partner closely with engineering and product to provide feedback from a researcher’s perspective
5+ years of experience in training deep learning models, not limited to LLM, ideally have worked with video, action, world models before
Proven track record of training SOTA models in an industry vertical
Strong experience in building and maintaining popular OSS repos.
Demonstrated ability to map user feedback from noise to key deliverables
Excellent prioritization skills and demonstrate execution efficiency
Strong sense of product GTM, demonstrate ability to balance strategic thinking with hands-on execution
Passion for staying up-to-date with SOTA AI research and trends
Experience with training transformer based models, and post-training/alignment
Hands on experience with PyTorch, distributed training, and tensor parallelism
5+ years of experience, including working at startups
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As the Documentation Engineer at LanceDB, you will make Lance and LanceDB easier to understand, adopt, and use in production. You'll work at the intersection of engineering, technical writing, and developer experience, turning complex data infrastructure concepts into clear explanations and working examples.
You'll partner closely with engineering, product, and our open-source community to document new features, build runnable examples, and keep guidance accurate as the product evolves. This role will own documentation as part of the product, with a particular emphasis on content that both developers and AI agents can discover, understand, and use reliably, while also building the tooling that keeps it accurate.
This is an opportunity to define how documentation supports a growing ecosystem of AI-assisted development and have a meaningful impact on how developers and AI agents learn and build with LanceDB.
Core documentation: Create and maintain quickstarts, tutorials, conceptual guides, API references, and troubleshooting documentation for Lance and LanceDB.
Documentation for AI agents: Design documentation agents can use reliably, including structured API descriptions, explicit prerequisites, complete examples, and clear error and recovery guidance. Evaluate it through agent-driven tasks to find where missing context or ambiguous instructions prevent agents from completing workflows correctly.
Runnable examples: Build examples and sample applications that demonstrate workflows such as data ingestion, vector search, filtering, indexing, and multimodal data management.
AI-assisted workflows: Use AI coding assistants and other AI tools to research the codebase, draft and revise content, generate examples, and automate maintenance, verifying outputs against product behavior and tested code.
Documentation tooling: Maintain documentation tooling and automate checks for code examples, API references, links, and content quality.
Partner closely with engineering and product teams to document new features alongside releases and keep existing guidance aligned with product changes.
Develop and continuously improve documentation structure, navigation, and search so developers can find the right information at each stage of their journey.
Use community questions, support feedback, and documentation usage to identify gaps and prioritize improvements.
Represent LanceDB with open-source contributors, reviewing documentation changes and establishing clear contribution guidelines.
8+ years of experience in documentation engineering, developer-focused technical writing, software engineering, or a related role
A proven track record of technical documentation, shown through a portfolio that explains complex concepts clearly and helps developers complete real tasks
Experience designing documentation for consumption by AI agents, with an understanding of how content structure, context, and API clarity affect agent performance
Demonstrated fluency with AI tooling, including coding assistants and LLM-based workflows, and sound judgment about how to validate their outputs
Strong Python skills and the ability to write, run, and debug examples using APIs and SDKs
Familiarity with databases, SQL, and data workflows, plus an interest in learning vector search and multimodal AI infrastructure
Experience with Git, pull requests, Markdown, and documentation workflows integrated with software development
Demonstrated ability to learn a product by reading code, experimenting with APIs, and collaborating with engineers
Strong editorial judgment, attention to technical accuracy, and the ability to prioritize and work independently
Experience documenting databases, data infrastructure, or AI and machine learning tools
Familiarity with LanceDB, Lance, Apache Arrow, or related technologies
Experience with TypeScript or Rust and documenting APIs across multiple languages
Experience with documentation platforms, generated API references, and CI-based testing of code examples
Experience building agent workflows, documenting tool interfaces, or evaluating agents against developer tasks
Contributions to open-source projects or experience supporting a developer community
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
As the Senior System Test Engineer at LanceDB, you will drive the correctness, reliability, and performance of Lance and LanceDB under demanding production conditions. You'll work at the intersection of distributed databases, systems engineering, and test infrastructure, building the frameworks and workloads that expose failures before our users encounter them.
You'll partner closely with engineering to turn architectural risks and real customer workloads into automated, reproducible tests, and to diagnose and fix complex failures. This is a hands-on role with ownership of system-level test strategy and execution, where you'll use AI tooling adeptly to accelerate development and investigation while maintaining rigorous validation.
This is an opportunity to define the quality bar for infrastructure powering the next generation of AI workloads, and to directly influence product architecture, release confidence, and the reliability our users depend on as they move from experimentation to production at scale.
System test strategy: Define and drive system-level testing across correctness, reliability, scalability, and performance, prioritizing the highest-risk behaviors in the platform.
Test frameworks and workloads: Build automated test frameworks and realistic workloads covering ingestion, concurrent reads and writes, indexing, querying, and dataset maintenance.
Data integrity and fault injection: Validate data integrity and documented consistency guarantees under concurrency, retries, partial failures, and recovery. Develop fault-injection and resilience tests for process crashes, network disruptions, storage errors, resource exhaustion, and interrupted operations.
Performance testing: Design load, stress, and long-running soak tests, and identify regressions in latency, throughput, resource usage, and search quality.
Upgrades and compatibility: Test upgrades, compatibility, and recovery scenarios to ensure the platform behaves predictably as software and workloads evolve.
Partner closely with engineers to investigate failures using logs, metrics, traces, and source code, reduce complex issues to minimal reproductions, and drive fixes and regression coverage.
Develop and continuously improve reproducible test environments, integrating reliable, actionable checks into CI and release workflows.
Use AI coding assistants and other AI tools to develop test harnesses, explore failure scenarios, analyze results, and accelerate debugging, independently verifying generated code and conclusions.
10+ years of experience in software engineering, system test engineering, or test infrastructure, with substantial experience building or validating distributed databases or large-scale data systems
Deep understanding of distributed databases, including consistency models, replication, partitioning, concurrency control, durability, and failure recovery
Proven track record of designing tests that verify system invariants and uncover data corruption, race conditions, and failures that emerge only under load or partial outages
Strong programming skills in Python and at least one systems language such as Rust, C++, Java, or Go, with the ability to build maintainable test infrastructure and debug product code
Hands-on experience with fault injection, performance testing, workload generation, and automated testing in distributed environments
Demonstrated ability to use AI tooling adeptly in daily engineering work, including coding assistants and agent-based workflows, with sound judgment about validating outputs
Experience with Linux, cloud infrastructure, containers, CI/CD, and observability tools
Ability to own ambiguous projects, prioritize risk, communicate findings clearly, and collaborate closely with engineers to drive issues to resolution
Experience testing database storage engines, query engines, vector search, or cloud object storage integrations
Familiarity with LanceDB, Lance, Apache Arrow, DataFusion, Parquet, or related data infrastructure
Experience with property-based testing, fuzzing, differential testing, or distributed consistency testing
Experience with Kubernetes, infrastructure as code, and large-scale test environment orchestration
Contributions to open-source databases, distributed systems, or testing frameworks
AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.
We're looking for a Senior Marketing Operations Manager to build the systems that turn LanceDB's marketing into measurable pipeline. Your job is to own the marketing tech stack, the data flowing through it, and the reporting that tells us what's working and what isn't.
You'll partner closely with our Head of RevOps, who owns the sales side of the CRM and the revenue process. Your domain is everything Marketing touches on the way to a qualified opportunity: lead capture, inbound lead routing, scoring, attribution, campaign and event operations, and the reporting that proves Marketing's impact on revenue.
One of the biggest opportunities in front of you is our open source. LanceDB sees millions of open source downloads per month, but we’re still early in our journey to reliably monetizing the open source funnel. You'll build the open-source qualified lead program that turns product and community signals into real sales outreach at the right now.
This is a 0-to-1 builder's role on a small, fast-moving team. You won't inherit a mature ops function. You'll design it, implement it, and keep it running as our go-to-market motion evolves.
Be the directly responsible individual (DRI) for our marketing systems, including HubSpot marketing automation, signal and enrichment tools (e.g. Common Room, Clay), advertising platforms (Google Ads, LinkedIn, Reddit), web analytics, and event and webinar tooling. You’ll help audit what we have, decide what we need, and implement (or cut) systems when necessary.
Build and run lead management: capture, enrichment, account assignment, routing, lifecycle stages, and scoring, so the right leads reach the right sellers quickly and nothing falls through the cracks.
Own event lead flow end to end, including registration, badge scans, enrichment, account assignment, and follow-up routed to the right owner within a clear SLA.
Build and run the open-source qualified lead program with Product, Engineering, and Marketing, turning open-source activity and account behavior into a scoring system that triggers outreach at the right moment.
Contribute to attribution and marketing reporting. Build the dashboards that show sourced and influenced pipeline by channel, program, and account, including a practical way to measure influence on founder-led and frontier lab deals that Marketing shapes but doesn't source.
Keep our data clean. Own marketing data hygiene, field governance (including Deal Source), deduplication, and enrichment in partnership with RevOps.
Run campaign operations: forms, landing pages, email programs, list uploads, and registration flows, including for flagship events like our Reverie summit.
Support the operating rhythm for Marketing: weekly funnel reporting against goal (MQLs, meetings booked and held, pipeline, and new ARR), budget tracking, and the data to support quarterly planning.
Partner with Revenue Marketing on ABM infrastructure, including target account lists, segmentation, intent and engagement signals, and program measurement.
Proactively find and ship improvements. Use AI and automation to make the whole team faster, from enrichment and research workflows to reporting.
You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams.
6-8 years in marketing operations, revenue operations, or growth operations at B2B technology companies, ideally related to developers or AI.
Someone who is AI-pilled. In addition to having a MOPs background, you’re comfortable with a GTM engineering approach and using the latest AI tools like Claude or Codex to build in intelligent automations.
Deep, hands-on experience with a CRM (SFDC or HubSpot) with a focus on marketing automation, workflows, lifecycle, reporting, and data modeling. You've built these systems yourself, not just managed an agency or team that did.
Experience with the modern GTM data stack, such as enrichment and workflow tools (e.g. Clay), signal platforms (e.g. Common Room), sales engagement (e.g. Apollo), ad platforms, and web analytics.
Hands-on experience designing lead routing and account assignment, including assigning event lead flow.
A track record of building attribution and pipeline reporting that leadership trusts and uses to make decisions.
Experience with PLG, open-source, or product-usage signals feeding a sales-led motion is a strong plus.
Proven ability to own work end to end without a team to support you. You have taste and strong pattern-matching for common problems, and you move quickly and with confidence.
A foundation-builder's mindset: you're motivated by creating process, systems, and measurement where little exists today, and you know when \"good enough and shipped\" beats \"perfect and pending.\"
You use AI thoughtfully as an operator, iterating on how it can improve and streamline your work and the team's.
Excellent communicator who can partner with founders, Sales, RevOps, and Engineering and move fast in a small, high-ownership team.
Bonus points for: experience at an AI, database, data infrastructure, or developer tools company, or scripting and API experience (Python, webhooks, reverse ETL).