From $225K/yr
Founding Engineer, Open Source Salary range — $225k – $300k | Equity — .15%-.35% | In-person NYC About Datalab Datalab trains models that read documents reliably at scale. The world's most important information is trapped in PDFs, scans, and files that can't easily be parsed, and getting it out correctly matters. From frontier AI labs processing training data to Fortune 500s like Siemens extracting decades of engineering records, Datalab is where businesses turn to when extraction has to be right. We’re at an 8-figure run rate with a team of 7. Anthropic is a customer. And we have hundreds more across FAANG, frontier AI labs, healthcare, finance, government, and legal. Our tools, chandra, surya, marker, and lift, have 70,000+ GitHub stars and broad developer mindshare. We're backed by founding members of OpenAI, FAIR, and Hugging Face. Role Overview We're looking for a Founding Engineer to develop and evangelize our open source repos. This includes chandra, surya, marker, pdftext, and lift, which collectively have over 70k Github stars. It also includes new tools we have yet to build and launch. As you work on our repos, you’ll also become the credible person to evangelize them. You’ll turn your own work into demos, benchmarks, tutorials, and launches. You’ll also support other launches across Datalab, especially when they touch open source components like the SDK. Our projects have real reach: 70k+ GitHub stars and users everywhere from frontier AI labs to Fortune 500s. Your job is to turn that reach into a thriving, engaged developer community through content, code, and showing up where developers already are. If you're the kind of engineer who’s energized by both building things and helping other people build, this is the role for you. Day to day: Own our open source repos and SDK: Drive chandra, surya, marker, lift, and our Python SDK forward as a hands-on contributor. Ship new features and improvements that the market cares about, help plan new versions, and own open-source launches end-to-end. This spans from the release itself to the demos, benchmarks, and content to evangelize the launch. Build demos and benchmarks. Build example apps, templates, and starter projects on top of Datalab's models and API that make it obvious what's possible and easy to remix. Publish benchmarks and evals that show, in code, how our models compare. Turn your work into content that teaches: Write technical blogs, guides, tutorials, and changelogs that help developers get started with marker, surya, chandra, lift, and our API, then go deep. Establish cookbooks and quickstart guides where we don't have them yet. Write content that helps developers differentiate between our platform / our models and our competitors (e.g., how to run evals). Close the loop with product: Bring developer feedback, friction points, and emerging needs back to engineering and product to help shape what we build next. Grow the community: Engage developers on GitHub, Discord, X, Reddit, and beyond. Celebrate contributors, answer questions, highlight cool projects, and add value in conversations rather than just promote. Be a visible, trusted voice for the platform online. Ideal Candidate You're a strong engineer who’s shipped things developers use, and you also love helping other people build. You know how to explain a complex idea simply, and you're as comfortable writing a tutorial or recording a demo as you are building a feature. 5+ years building production software, with meaningful open-source contributions Strong hands-on engineering skills; fluent in Python, shipping real, reproducible, well-tested code Track record of owning code end-to-end - design, implementation, release, and maintenance Able to create credible technical content - blogs, docs, tutorials, demos, or video - that developers actually read, watch, and use, because you understand the system deeply Comfortable representing your work publicly, whether in writing, on camera, or in online communities Comfortable with an early-stage startup - self-directed, hands-on, and happy to balance depth with shipping velocity Bonus points if you: Have experience with OCR, document AI, or structured extraction Have contributed to or maintained a widely-used open-source ML/vision/NLP project Have a growing audience on YouTube, X, or Twitch, or an active presence in developer communities Have used (and loved) marker, surya, chandra, or lift Interview process 30-minute video call to evaluate fit 1.5-hour in person meeting to build an architecture together Async code sharing + review phase (not a take-home project, you just share some code samples) Culture fit interviews with the team At this stage of the company, every interview is somewhat custom, so these phases may be rearranged slightly.