Datalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.
Because we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.
We're a small, founder-led team. You'll have real influence on how we sell and what we build.
We’re looking for a fullstack engineer who wants to build the interfaces, tools, and infrastructure that help developers and enterprises use our models. You’ll work across the stack to shape how people interact with OCR, extraction, and document-understanding systems. That includes building core inference workflows, creating intuitive UI for complex parsing tasks, and improving the developer experience across our open-source repos and API.
This is a high-ownership role that blends engineering, product thinking, and community engagement. You will work closely with the founders and the rest of the team to ship features, improve performance, and make our technology accessible to a global community of builders.
As a small and fast-moving team, roles are fluid. You should enjoy working across backend, frontend, performance, and user-facing surfaces. Your work will directly influence how teams evaluate and deploy our models.
Ship features to our open source repos, API, and internal tooling.
Design and build frontend features that make document parsing more interactive and understandable.
Optimize inference performance and improve the reliability of our frameworks.
Engage with the community on Github and Discord by collecting feedback, debugging issues, and identifying opportunities for improvement.
Support customer implementations with technical guidance and troubleshooting when needed.
You thrive at the intersection of engineering, product, and user experience. You like working close to real users and making technical systems feel simple and intuitive. You operate with autonomy and ownership and enjoy moving quickly in an environment where you can directly influence outcomes.
We’re eager to work with someone who has:
5+ years of fullstack development experience building APIs and/or developer-focused products.
Experience building in early-stage startup environments.
Shipped and maintained production systems serving high-volume traffic.
Experience building with LLMs and agentic workflows.
Bonus points:
Experience with Python ML frameworks (PyTorch, Transformers, etc.) or interest in training models.
Familiarity with document processing, computer vision, or OCR systems.
Maintained or contributed to open-source projects with active communities
Experience writing technical content or demos to showcase your work.
30-minute video call to evaluate fit
90-minute live architecture discussion
Culture fit interviews with the team
At this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.
Datalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.
If you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to.
","descriptionPlain":"ABOUT DATALAB\n\nDatalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.\n\nBecause we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.\n\nWe're a small, founder-led team. You'll have real influence on how we sell and what we build.\n\n \n\n\nROLE OVERVIEW\n\nWe’re looking for a fullstack engineer who wants to build the interfaces, tools, and infrastructure that help developers and enterprises use our models. You’ll work across the stack to shape how people interact with OCR, extraction, and document-understanding systems. That includes building core inference workflows, creating intuitive UI for complex parsing tasks, and improving the developer experience across our open-source repos and API.\n\nThis is a high-ownership role that blends engineering, product thinking, and community engagement. You will work closely with the founders and the rest of the team to ship features, improve performance, and make our technology accessible to a global community of builders.\n\nAs a small and fast-moving team, roles are fluid. You should enjoy working across backend, frontend, performance, and user-facing surfaces. Your work will directly influence how teams evaluate and deploy our models.\n\n\n\n\nDAY TO DAY, YOU WILL:\n\n - Ship features to our open source repos, API, and internal tooling.\n\n - Design and build frontend features that make document parsing more interactive and understandable.\n\n - Optimize inference performance and improve the reliability of our frameworks.\n\n - Engage with the community on Github and Discord by collecting feedback, debugging issues, and identifying opportunities for improvement.\n\n - Support customer implementations with technical guidance and troubleshooting when needed.\n\n\n\n\nIDEAL CANDIDATE\n\nYou thrive at the intersection of engineering, product, and user experience. You like working close to real users and making technical systems feel simple and intuitive. You operate with autonomy and ownership and enjoy moving quickly in an environment where you can directly influence outcomes.\n\nWe’re eager to work with someone who has:\n\n - 5+ years of fullstack development experience building APIs and/or developer-focused products.\n\n - Experience building in early-stage startup environments.\n\n - Shipped and maintained production systems serving high-volume traffic.\n\n - Experience building with LLMs and agentic workflows.\n\nBonus points:\n\n - Experience with Python ML frameworks (PyTorch, Transformers, etc.) or interest in training models.\n\n - Familiarity with document processing, computer vision, or OCR systems.\n\n - Maintained or contributed to open-source projects with active communities\n\n - Experience writing technical content or demos to showcase your work.\n\n\n\n\nINTERVIEW PROCESS\n\n 1. 30-minute video call to evaluate fit\n\n 2. 90-minute live architecture discussion\n\n 3. Culture fit interviews with the team\n\nAt this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.\n\n\n\n\nEQUAL OPPORTUNITY\n\nDatalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.\n\nIf you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to."},{"id":"7fac4a34-86d6-47cd-b44c-12ac0a7cb174","title":"Research Engineer","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"New York Office","secondaryLocations":[],"publishedAt":"2026-07-06T18:40:31.390+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York"}},"jobUrl":"https://jobs.ashbyhq.com/datalab/7fac4a34-86d6-47cd-b44c-12ac0a7cb174","applyUrl":"https://jobs.ashbyhq.com/datalab/7fac4a34-86d6-47cd-b44c-12ac0a7cb174/application","descriptionHtml":"Datalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.
Because we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.
We're a small, founder-led team. You'll have real influence on how we sell and what we build.
We're looking for a Research Engineer to own problems end to end across our models, inference service, and product. You won't just train a model and hand it off. You'll take it from training through benchmarking, into our inference stack, and work with the team to integrate it into our products.
We're a small team that has shipped the current state of the art OCR model, Chandra. Our models collectively have 70k+ Github stars. Our tools are used internally at frontier AI labs like Anthropic, and Fortune 500 enterprises like Siemens.
Our team focuses on training small, efficient models that outperform much larger LLMs on domain-specific tasks (like OCR, structured extraction, tables). We move fast, prioritize practical results, and build tools that are open, reproducible, and built to last. You'll test hypotheses quickly, iterate on results, and balance experimental rigor with shipping to customers.
A typical project might look like: identify a gap in extraction quality on long documents, train and benchmark a new model, optimize it for inference, and work with the team to ship it to users. Concretely:
Train and evaluate models: Train task-specific models (OCR, layout, text recognition, extraction). Explore architectures and training strategies to optimize task performance. This includes our open source models, like Marker, Surya, and Chandra.
Optimize inference: Profile and accelerate model inference across different hardware setups (H100s, B200s, L40s, CPUs).
Ship to product: Work with the team to integrate models into our API and product, helping define how new capabilities surface for end users. You will be involved from model training through integration, although your work will be weighted much more towards the model side than the product side.
Create and maintain datasets: Source, design, and clean datasets for supervised and synthetic training; create reproducible pipelines for data versioning and evaluation.
Experiment and benchmark: Run ablations, track metrics, and publish findings that inform model design and internal research direction.
Engage with users and partners: Occasionally join calls or Slack threads to better understand customer needs and inform your work.
You've shipped models that made it into production. You understand how to balance exploration with delivery, and how to turn research insights into products people actually use.
3+ years experience training, fine-tuning, and evaluating deep learning models
Trained at least one production-grade model or system used in real-world applications
Deep expertise in PyTorch and Python, with strong fundamentals in deep learning (optimization, evaluation, architecture design)
Comfortable with data engineering, benchmarking, and performance profiling across hardware setups
Comfortable with an early stage startup - balance running ablations/benchmarks with shipping velocity
Bonus points if you:
Have experience with OCR, document AI, or structured extraction
Have published work, whether that's a paper, a benchmark report, or a deep technical blog post
Have been a major contributor to open-source projects, especially in ML, vision, or NLP
Enjoy writing about your work and sharing learnings with the community
A 30-minute video call to evaluate fit
90-minute live architecture discussion
Culture fit interview/team meeting
At this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.
Datalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.
If you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to.
","descriptionPlain":"ABOUT DATALAB\n\nDatalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.\n\nBecause we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.\n\nWe're a small, founder-led team. You'll have real influence on how we sell and what we build.\n\n \n\n\nROLE OVERVIEW\n\nWe're looking for a Research Engineer to own problems end to end across our models, inference service, and product. You won't just train a model and hand it off. You'll take it from training through benchmarking, into our inference stack, and work with the team to integrate it into our products.\n\nWe're a small team that has shipped the current state of the art OCR model, Chandra. Our models collectively have 70k+ Github stars. Our tools are used internally at frontier AI labs like Anthropic, and Fortune 500 enterprises like Siemens.\n\nOur team focuses on training small, efficient models that outperform much larger LLMs on domain-specific tasks (like OCR, structured extraction, tables). We move fast, prioritize practical results, and build tools that are open, reproducible, and built to last. You'll test hypotheses quickly, iterate on results, and balance experimental rigor with shipping to customers.\n\n\n\n\nDAY TO DAY:\n\nA typical project might look like: identify a gap in extraction quality on long documents, train and benchmark a new model, optimize it for inference, and work with the team to ship it to users. Concretely:\n\n - Train and evaluate models: Train task-specific models (OCR, layout, text recognition, extraction). Explore architectures and training strategies to optimize task performance. This includes our open source models, like Marker, Surya, and Chandra.\n\n - Optimize inference: Profile and accelerate model inference across different hardware setups (H100s, B200s, L40s, CPUs).\n\n - Ship to product: Work with the team to integrate models into our API and product, helping define how new capabilities surface for end users. You will be involved from model training through integration, although your work will be weighted much more towards the model side than the product side.\n\n - Create and maintain datasets: Source, design, and clean datasets for supervised and synthetic training; create reproducible pipelines for data versioning and evaluation.\n\n - Experiment and benchmark: Run ablations, track metrics, and publish findings that inform model design and internal research direction.\n\n - Engage with users and partners: Occasionally join calls or Slack threads to better understand customer needs and inform your work.\n\n\n\n\nIDEAL CANDIDATE\n\nYou've shipped models that made it into production. You understand how to balance exploration with delivery, and how to turn research insights into products people actually use.\n\n - 3+ years experience training, fine-tuning, and evaluating deep learning models\n\n - Trained at least one production-grade model or system used in real-world applications\n\n - Deep expertise in PyTorch and Python, with strong fundamentals in deep learning (optimization, evaluation, architecture design)\n\n - Comfortable with data engineering, benchmarking, and performance profiling across hardware setups\n\n - Comfortable with an early stage startup - balance running ablations/benchmarks with shipping velocity\n\nBonus points if you:\n\n - Have experience with OCR, document AI, or structured extraction\n\n - Have published work, whether that's a paper, a benchmark report, or a deep technical blog post\n\n - Have been a major contributor to open-source projects, especially in ML, vision, or NLP\n\n - Enjoy writing about your work and sharing learnings with the community\n\n\n\n\nINTERVIEW PROCESS\n\n - A 30-minute video call to evaluate fit\n\n - 90-minute live architecture discussion\n\n - Culture fit interview/team meeting\n\nAt this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.\n\n\n\n \n\n\nEQUAL OPPORTUNITY\n\nDatalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.\n\nIf you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to."},{"id":"66231ee5-be4a-46ee-abd5-7b4bc65fbdea","title":"Founding Marketer","department":"Marketing","team":"Marketing","employmentType":"FullTime","location":"New York Office","secondaryLocations":[],"publishedAt":"2026-10-13T01:47:25.609+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York"}},"jobUrl":"https://jobs.ashbyhq.com/datalab/66231ee5-be4a-46ee-abd5-7b4bc65fbdea","applyUrl":"https://jobs.ashbyhq.com/datalab/66231ee5-be4a-46ee-abd5-7b4bc65fbdea/application","descriptionHtml":"Datalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.
Because we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.
We're a small, founder-led team. You'll have real influence on how we sell and what we build.
Marketing at Datalab already has real infrastructure. We run a combined launch and content calendar with a defined playbook - channels, owners, and cadence for every release - and we ship two to three launches a week against it. No one here works on marketing full-time, so nobody owns the bigger question of how we show up when a developer, a buyer, or an AI agent looks for a document parser.
You’d be our first dedicated marketing hire. You’d inherit a working system rather than a blank page, and your job would be to run it reliably, then make it considerably more ambitious. The natural place to build from is our open source work, which reaches a wide audience and currently does less for us than it could.
This is a hands-on role. On a team of 15, you’ll write the blog post, draft the tweets, restructure the docs page, brief the agency if we hire one, and pull the numbers yourself. We’re looking for someone who's done this work well somewhere and wants to keep doing it, rather than direct someone else who does.
This role has strong potential to grow into a leadership role as we hire more people into the marketing team.
An increasing share of our buyers never touch a search results page; they ask a model. We want someone who takes seriously how Datalab gets recommended by LLMs and agents, how our site, docs, and benchmarks are structured, where models pull their answers from, and how we measure it all. Nobody has fully figured this out yet, and we’d like you to be the person who figures it out.
If you want to inherit a brand book, a content team, and a defined category, this isn’t the right role. If you want to shape how a technical category gets talked about, with real customers and real distribution to work from, it’s a good one.
A typical week might look like: shipping the launch post for a new open source model, ghostwriting a thread for one of our researchers, editing a vertical explainer on tracked-changes extraction for legal teams, restructuring a docs page so a model can actually cite it, answering a sharp question in Discord, and digging into why organic signups dipped last week.
Own our model launches, especially open source. Every release - Chandra, Surya, Marker, Lift, and what comes next - should land. You’ll build the assets (launch posts, benchmark write-ups, demos, docs), coordinate the team’s posts, work HN and Reddit and the ML community, handle press where it’s worth handling, and run the retro afterward so the next one lands harder.
Take over and scale the content engine. Inherit the calendar, tighten the cadence, and own the strategy and much of the writing. Educational content for developers evaluating us, and vertical-specific content for the buyers who need to see their own documents in the example - legal, healthcare, financial services, insurance, logistics, government.
Ghostwrite for the team. Our engineers and researchers know things worth writing about and mostly won’t write it themselves. You’ll draft in their voice, in a way they’re glad to put their name on, and turn the team into a distribution channel.
Make Datalab legible to LLMs and agents. Structure the site, docs, and benchmark content so that models recommend us when someone asks how to parse a PDF. Own the technical work behind it, figure out what “ranking” even means in this context, and build a way to measure whether it’s working.
Treat the website as our front door. Most of our revenue starts with someone landing on datalab.to. Own the narrative, structure, and conversion path of the site and the top of our docs, working with engineering to ship changes.
Own the demand channels and the budget behind them. SEO, paid, landing pages, conversion. Propose and manage spend, and own the numbers end to end—partnering with our Business Operations hire on funnel reporting and attribution so we know which channels actually produce revenue.
Arm the sales team. Build the case studies, benchmark comparisons, one-pagers, and decks our AE and solutions engineers need in live deals. Our strongest proof points are customers with hard extraction problems; turn those into assets that help close business and double as vertical content.
Show up in the community. Our Discord and GitHub are our largest owned audience. Set the tone there, spot what people keep asking for, and turn recurring questions into documentation and content. Represent us at conferences, meetups, and on podcasts — and build the speaking and events motion, including getting our researchers in front of the right rooms.
Track where AI is going and translate it into positioning. Agents, evals, context engineering, the shifting shape of the document AI market. Give us a point of view on what it means for how we talk about ourselves, not just a news summary.
Feed the loop back to product and sales. What messaging converts, what objections keep surfacing, what the community keeps asking for — bring structured signal to the founder and the GTM team on a regular cadence.
By month 3
You’ve owned at least one model launch end to end, and it outperformed the last comparable release on a metric we agreed on in advance.
The launch and content calendar is shipping on schedule without slipping, with the team contributing under their own names.
Baselines are in place: organic traffic, signups by source, LLM-recommendation visibility, and a first read on what’s working.
By month 12
Inbound is meaningfully up and attributable, with at least one channel you built from nothing producing consistent pipeline.
The launch playbook is documented well enough that a new hire could run a release with it.
Datalab shows up when a model is asked how to parse documents at scale — and you can prove it moved because of work you did.
You’ve built this before at a company where it mattered. You write well and fast, and you’d rather ship a good post today than a perfect one next month. You’re comfortable being measured - you’d rather have a number attached to your work than not. You’re ambitious about where this goes and want more scope over time, but you’re not waiting for it before you start doing the work.
You’re technical enough to hold your own. Our audience is engineers, ML leads, and CTOs, and they can tell instantly when marketing doesn’t understand the product. You should be able to read a benchmark table and know what it means, run our models yourself, and write something a skeptical developer on Hacker News would find useful rather than annoying.
You’re collaborative and low-ego. You’ll be pulling engineers into launches, asking researchers for time, and putting your writing out under someone else’s name regularly. You make the call that’s right for the company, and you’re glad when the work lands even if the byline isn’t yours.
We’re eager to work with someone who has:
7+ years in marketing, with meaningful time owning content, launches, and demand generation for a technical or developer-facing product.
Run launches at a high level - coordinated messaging, assets, press, and community across a real release cycle - and can point to both the results and the parts you personally shipped.
Built a content program from strategy through published work, and written most of it yourself.
Owned SEO and paid channels directly, including budget, with numbers on what moved and what didn’t.
Operated in early-stage environments where the playbook didn’t exist and the team was small.
Can use AI tools like Claude and Cowork effectively to streamline your work and build content systems.
Bonus points if you:
Have marketed an open source project or built inside a developer community (GitHub, HN, Discord, ML Twitter).
Have worked on document AI, OCR, IDP, or data extraction - or adjacent infrastructure and developer tooling.
Have run experiments in LLM/AI-assistant discoverability and have opinions about what actually works.
Have ghostwritten for founders or technical leaders and have samples you can talk through.
Are comfortable enough with design to get a launch asset out the door yourself, and know when to bring in help.
Have marketed into regulated or public-sector buyers, where the proof burden is higher.
Have hired contractors, agencies, or freelancers and gotten good work out of them.
Want to build and lead a marketing team, and can tell us why you think you’re ready for that next.
Are active in the AI community and actually use open-source models and tools.
A 30-minute video call with the founder to evaluate fit.
A 1-hour conversation going deep on a launch you have run, channel evaluation, and selling technical products
A 2-hour onsite in NYC, working through: traffic and funnel data, a live editing and ghostwriting session using our actual drafts and voices, and a discussion of a short strategy brief.
Final conversations with the team.
We expect you to use AI tools throughout, including on the prepared brief - we use them constantly and want to see how you work, not watch you pretend you don’t.
At this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.
Datalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.
If you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to.
","descriptionPlain":"ABOUT DATALAB\n\nDatalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.\n\nBecause we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.\n\nWe're a small, founder-led team. You'll have real influence on how we sell and what we build.\n\n\n\n\nROLE OVERVIEW\n\nMarketing at Datalab already has real infrastructure. We run a combined launch and content calendar with a defined playbook - channels, owners, and cadence for every release - and we ship two to three launches a week against it. No one here works on marketing full-time, so nobody owns the bigger question of how we show up when a developer, a buyer, or an AI agent looks for a document parser.\n\nYou’d be our first dedicated marketing hire. You’d inherit a working system rather than a blank page, and your job would be to run it reliably, then make it considerably more ambitious. The natural place to build from is our open source work, which reaches a wide audience and currently does less for us than it could.\n\nThis is a hands-on role. On a team of 15, you’ll write the blog post, draft the tweets, restructure the docs page, brief the agency if we hire one, and pull the numbers yourself. We’re looking for someone who's done this work well somewhere and wants to keep doing it, rather than direct someone else who does.\n\nThis role has strong potential to grow into a leadership role as we hire more people into the marketing team.\n\nAn increasing share of our buyers never touch a search results page; they ask a model. We want someone who takes seriously how Datalab gets recommended by LLMs and agents, how our site, docs, and benchmarks are structured, where models pull their answers from, and how we measure it all. Nobody has fully figured this out yet, and we’d like you to be the person who figures it out.\n\nIf you want to inherit a brand book, a content team, and a defined category, this isn’t the right role. If you want to shape how a technical category gets talked about, with real customers and real distribution to work from, it’s a good one.\n\n\n\n\nDAY TO DAY YOU WILL:\n\nA typical week might look like: shipping the launch post for a new open source model, ghostwriting a thread for one of our researchers, editing a vertical explainer on tracked-changes extraction for legal teams, restructuring a docs page so a model can actually cite it, answering a sharp question in Discord, and digging into why organic signups dipped last week.\n\n - Own our model launches, especially open source. Every release - Chandra, Surya, Marker, Lift, and what comes next - should land. You’ll build the assets (launch posts, benchmark write-ups, demos, docs), coordinate the team’s posts, work HN and Reddit and the ML community, handle press where it’s worth handling, and run the retro afterward so the next one lands harder.\n\n - Take over and scale the content engine. Inherit the calendar, tighten the cadence, and own the strategy and much of the writing. Educational content for developers evaluating us, and vertical-specific content for the buyers who need to see their own documents in the example - legal, healthcare, financial services, insurance, logistics, government.\n\n - Ghostwrite for the team. Our engineers and researchers know things worth writing about and mostly won’t write it themselves. You’ll draft in their voice, in a way they’re glad to put their name on, and turn the team into a distribution channel.\n\n - Make Datalab legible to LLMs and agents. Structure the site, docs, and benchmark content so that models recommend us when someone asks how to parse a PDF. Own the technical work behind it, figure out what “ranking” even means in this context, and build a way to measure whether it’s working.\n\n - Treat the website as our front door. Most of our revenue starts with someone landing on datalab.to http://datalab.to. Own the narrative, structure, and conversion path of the site and the top of our docs, working with engineering to ship changes.\n\n - Own the demand channels and the budget behind them. SEO, paid, landing pages, conversion. Propose and manage spend, and own the numbers end to end—partnering with our Business Operations hire on funnel reporting and attribution so we know which channels actually produce revenue.\n\n - Arm the sales team. Build the case studies, benchmark comparisons, one-pagers, and decks our AE and solutions engineers need in live deals. Our strongest proof points are customers with hard extraction problems; turn those into assets that help close business and double as vertical content.\n\n - Show up in the community. Our Discord and GitHub are our largest owned audience. Set the tone there, spot what people keep asking for, and turn recurring questions into documentation and content. Represent us at conferences, meetups, and on podcasts — and build the speaking and events motion, including getting our researchers in front of the right rooms.\n\n - Track where AI is going and translate it into positioning. Agents, evals, context engineering, the shifting shape of the document AI market. Give us a point of view on what it means for how we talk about ourselves, not just a news summary.\n\n - Feed the loop back to product and sales. What messaging converts, what objections keep surfacing, what the community keeps asking for — bring structured signal to the founder and the GTM team on a regular cadence.\n\n\nWHAT SUCCESS LOOKS LIKE\n\nBy month 3\n\n - You’ve owned at least one model launch end to end, and it outperformed the last comparable release on a metric we agreed on in advance.\n\n - The launch and content calendar is shipping on schedule without slipping, with the team contributing under their own names.\n\n - Baselines are in place: organic traffic, signups by source, LLM-recommendation visibility, and a first read on what’s working.\n\nBy month 12\n\n - Inbound is meaningfully up and attributable, with at least one channel you built from nothing producing consistent pipeline.\n\n - The launch playbook is documented well enough that a new hire could run a release with it.\n\n - Datalab shows up when a model is asked how to parse documents at scale — and you can prove it moved because of work you did.\n\n\nIDEAL CANDIDATE\n\nYou’ve built this before at a company where it mattered. You write well and fast, and you’d rather ship a good post today than a perfect one next month. You’re comfortable being measured - you’d rather have a number attached to your work than not. You’re ambitious about where this goes and want more scope over time, but you’re not waiting for it before you start doing the work.\n\nYou’re technical enough to hold your own. Our audience is engineers, ML leads, and CTOs, and they can tell instantly when marketing doesn’t understand the product. You should be able to read a benchmark table and know what it means, run our models yourself, and write something a skeptical developer on Hacker News would find useful rather than annoying.\n\nYou’re collaborative and low-ego. You’ll be pulling engineers into launches, asking researchers for time, and putting your writing out under someone else’s name regularly. You make the call that’s right for the company, and you’re glad when the work lands even if the byline isn’t yours.\n\nWe’re eager to work with someone who has:\n\n - 7+ years in marketing, with meaningful time owning content, launches, and demand generation for a technical or developer-facing product.\n\n - Run launches at a high level - coordinated messaging, assets, press, and community across a real release cycle - and can point to both the results and the parts you personally shipped.\n\n - Built a content program from strategy through published work, and written most of it yourself.\n\n - Owned SEO and paid channels directly, including budget, with numbers on what moved and what didn’t.\n\n - Operated in early-stage environments where the playbook didn’t exist and the team was small.\n\n - Can use AI tools like Claude and Cowork effectively to streamline your work and build content systems.\n\nBonus points if you:\n\n - Have marketed an open source project or built inside a developer community (GitHub, HN, Discord, ML Twitter).\n\n - Have worked on document AI, OCR, IDP, or data extraction - or adjacent infrastructure and developer tooling.\n\n - Have run experiments in LLM/AI-assistant discoverability and have opinions about what actually works.\n\n - Have ghostwritten for founders or technical leaders and have samples you can talk through.\n\n - Are comfortable enough with design to get a launch asset out the door yourself, and know when to bring in help.\n\n - Have marketed into regulated or public-sector buyers, where the proof burden is higher.\n\n - Have hired contractors, agencies, or freelancers and gotten good work out of them.\n\n - Want to build and lead a marketing team, and can tell us why you think you’re ready for that next.\n\n - Are active in the AI community and actually use open-source models and tools.\n\n\nINTERVIEW PROCESS\n\n 1. A 30-minute video call with the founder to evaluate fit.\n\n 2. A 1-hour conversation going deep on a launch you have run, channel evaluation, and selling technical products\n\n 3. A 2-hour onsite in NYC, working through: traffic and funnel data, a live editing and ghostwriting session using our actual drafts and voices, and a discussion of a short strategy brief.\n\n 4. Final conversations with the team.\n\nWe expect you to use AI tools throughout, including on the prepared brief - we use them constantly and want to see how you work, not watch you pretend you don’t.\n\nAt this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.\n\n\n\n\nEQUAL OPPORTUNITY\n\nDatalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.\n\nIf you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to."},{"id":"c43115f2-9cb0-4bbc-807a-c299bc7e83a1","title":"Enterprise Solutions Engineer","department":"Sales","team":"Sales","employmentType":"FullTime","location":"New York Office","secondaryLocations":[],"publishedAt":"2026-10-09T13:47:38.297+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"New York","addressCountry":"United States","addressLocality":"New York"}},"jobUrl":"https://jobs.ashbyhq.com/datalab/c43115f2-9cb0-4bbc-807a-c299bc7e83a1","applyUrl":"https://jobs.ashbyhq.com/datalab/c43115f2-9cb0-4bbc-807a-c299bc7e83a1/application","descriptionHtml":"Picture this: an AE walks into the room and introduces you. You do the work to understand the prospect and what it takes to win. You get the deal to a product and technical yes. Then someone else closes it, and the win goes on their record, not yours.
Most solutions engineer roles stop at the demo. This one doesn't. The win is yours.
At Datalab, solutions engineers are the sales team. The people who know the product best should be the ones asking for the signature and talking price.
You'd be one of the first hires on this team, working directly with our leadership team.
Datalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.
Because we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.
We're a small, founder-led team. You'll have real influence on how we sell and what we build.
You own the deal. Everything below is yours, with the whole team as backup when you want it.
Own opportunities from first conversation to signature.
Win the technical evaluation: scope the problem, run the proof of concept and design the deployment, whether API, VPC or on-prem.
Make the business case to the customer's leadership, not just their engineers.
Own pricing and commercial terms.
Bring what you learn back to product and engineering, where you'll have real say and sway over what we build.
We care about three things: you're credible with engineers, you can hold your own with a C-suite or other executives, and you can close.
We're eager to work with someone who has:
Significantly contributed to closing large (six-figure) opportunities, as an AE selling a technical product or as an SE who carried enterprise deals to signature, and can give concrete examples of what you did.
Credibility with engineers. You can read API docs, write a script, debug a deployment, and talk to an ML engineer and a CISO on the same day.
Presence with executives. You can hold your own in a room with a C-suite buyer and make the business case.
A feel for how enterprise deals move: multiple stakeholders, security reviews, procurement, budget cycles.
A drive to win. You'd rather own the outcome than hand it off.
Comfort without a map. Early stage, no playbook. That sounds fun to you.
None of these are required. Each one shortens your ramp.
Document AI, OCR, parsing, extraction, or LLM evals.
Enterprise sales experience in financial services or insurance, or with AI native companies.
On-prem or regulated deployments.
Intro call
Deal walkthrough
Take-home project
Conversations with the team
Datalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.
If you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to.
","descriptionPlain":"Picture this: an AE walks into the room and introduces you. You do the work to understand the prospect and what it takes to win. You get the deal to a product and technical yes. Then someone else closes it, and the win goes on their record, not yours.\n\nMost solutions engineer roles stop at the demo. This one doesn't. The win is yours.\n\nAt Datalab, solutions engineers are the sales team. The people who know the product best should be the ones asking for the signature and talking price.\n\nYou'd be one of the first hires on this team, working directly with our leadership team.\n\n\n\n\nABOUT DATALAB\n\nDatalab is an applied research lab in New York. We train our own models to turn documents into data you can verify: scans, dense tables, handwriting, the pages other tools quietly get wrong.\n\nBecause we own the models, a customer's hard problem becomes a roadmap conversation, not a feature request. Our open-source work (Marker, Surya, Chandra) has more than 75,000 GitHub stars. Customers include Anthropic, ElevenLabs, Gamma, Reflection AI, and Legora, along with insurers, financial firms, and healthcare companies. We deploy anywhere from our API to fully air-gapped on-prem.\n\nWe're a small, founder-led team. You'll have real influence on how we sell and what we build.\n\n\n\n\nDAY TO DAY\n\nYou own the deal. Everything below is yours, with the whole team as backup when you want it.\n\n - Own opportunities from first conversation to signature.\n\n - Win the technical evaluation: scope the problem, run the proof of concept and design the deployment, whether API, VPC or on-prem.\n\n - Make the business case to the customer's leadership, not just their engineers.\n\n - Own pricing and commercial terms.\n\n - Bring what you learn back to product and engineering, where you'll have real say and sway over what we build.\n \n \n\n\nIDEAL CANDIDATE\n\nWe care about three things: you're credible with engineers, you can hold your own with a C-suite or other executives, and you can close.\n\nWe're eager to work with someone who has:\n\n - Significantly contributed to closing large (six-figure) opportunities, as an AE selling a technical product or as an SE who carried enterprise deals to signature, and can give concrete examples of what you did.\n\n - Credibility with engineers. You can read API docs, write a script, debug a deployment, and talk to an ML engineer and a CISO on the same day.\n\n - Presence with executives. You can hold your own in a room with a C-suite buyer and make the business case.\n\n - A feel for how enterprise deals move: multiple stakeholders, security reviews, procurement, budget cycles.\n\n - A drive to win. You'd rather own the outcome than hand it off.\n\n - Comfort without a map. Early stage, no playbook. That sounds fun to you.\n\n\n\n\nBONUS POINTS\n\nNone of these are required. Each one shortens your ramp.\n\n - Document AI, OCR, parsing, extraction, or LLM evals.\n\n - Enterprise sales experience in financial services or insurance, or with AI native companies.\n\n - On-prem or regulated deployments.\n\n\n\n\nINTERVIEW PROCESS\n\n 1. Intro call\n\n 2. Deal walkthrough\n\n 3. Take-home project\n\n 4. Conversations with the team\n\n\n\nDatalab is an equal opportunity employer. We do not discriminate on the basis of any characteristic protected by federal, state, or local law.\n\nIf you need an accommodation to participate in our hiring process, or if you believe you have experienced discrimination or harassment at any point in it, contact operations@datalab.to."}],"apiVersion":"1"}