Who we are

Foundation models have transformed text and images, but structured data - the largest and most consequential data modality in the world - has remained untouched. Tables power every clinical trial, every financial model, every scientific experiment, every business decision. No one has built a foundation model that truly understands them.

Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening - and we're hiring the team that makes it.

Momentum: We pioneered tabular foundation models and are now the world-leading organization in structured data ML. Our TabPFN v2 model was published in Nature and set a new state-of-the-art for tabular machine learning. Since its release, we've scaled model capabilities more than 20x, reached 3M+ downloads, 6,000+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical trial decisions with BostonGene.

The hardest work is in front of us. We're scaling tabular foundation models to handle millions of rows, thousands of features, real-time inference, and entirely new data modalities - while building the infrastructure to deploy them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level.

Our team: We’re a small, highly selective team of 20+ engineers, researchers and GTM specialists, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G-Research, Jane Street, Goldman Sachs, and CERN, led by Frank Hutter, Noah Hollmann and Sauraj Gambhir and advised by world-leading AI researchers such as Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, create top-tier research, and hold each other to an extremely high bar.

What’s Next: In 2025, we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here which makes this an optimal time to join.

Your Mission

Documentation is a product at Prior Labs. You will build our technical documentation practice from the ground up, improving and expanding what we have, establishing an AI-assisted docs process, and ensuring that our open-source repositories, API, and products are always well-documented. You will be the person who makes it easy for a machine learning engineer to go from zero to productive with TabPFN in minutes, not hours.

What You'll Do

• Audit, rewrite, and expand our existing documentation across our open-source repositories (TabPFN, tabpfn-extensions, tabpfn-client) and API reference.

• Author new content including tutorials, how-to guides, conceptual explainers, and real-world use case examples targeted for ML practitioners and data scientists.

• Design and implement a modern, AI-assisted documentation workflow using our current stack (GitHub, Linear, Mintlify) to keep docs in sync with code changes.

• Work closely with engineers to translate complex model behaviour and API design into clear, accurate developer-facing content.

• Contribute Mintlify-compatible Markdown that follows our style and renders correctly in our docs site.

• Own the docs roadmap and prioritise improvements based on user feedback and product changes.

What We Are Looking For

• 3+ years of technical writing experience, ideally for developer tools, ML platforms, or open-source projects alongside engineering teams in a fast-moving startup or research environment.

• Hands-on experience with docs-as-code workflows: Markdown, Git, pull requests, and CI/CD-integrated documentation pipelines.

• Strong technical writing fundamentals: clear structure, consistent terminology, audience-aware tone.

• Familiarity with AI-assisted writing tools and experience integrating them into documentation workflows.

• Possess university level Python to produce relevant example code to be used in live documentation.

• Ability to independently research and synthesise technical concepts from source code, papers, and conversations with engineers.

• Solid understanding of machine learning concepts, especially supervised and unsupervised learning, enough to write confidently for an ML-practitioner audience.

• Understanding of open-source contribution dynamics and how to write documentation that serves both core users and community contributors.

• You are proactive: you spot gaps before they become problems and propose solutions without being asked.

What Is Nice to Have

• Prior experience setting up a documentation system from scratch, including style guides and contribution guidelines.

• Experience with video or visual content creation (screencasts, diagrams, interactive notebooks) as a complement to written docs.

Life at Prior Labs

We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with.

We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you.

Our Commitments

We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box."

We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.

We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.