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.
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 and researchers, 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 modality shift in AI is happening - and we're hiring the team that makes it.
You'll join our data science team working with an entirely new class of AI models. As a Data Scientist at Prior Labs, you'll be the critical link between our foundation models and real-world applications — experimenting hands-on with our tabular foundation models (including TabPFN) to uncover new applications, working directly with customers to show how native tabular AI solves problems traditional methods can't, and translating what you learn back into our product roadmap.
Applied Data Science & Experimentation: Identify high-impact use cases for TFMs and build proof-of-concepts that showcase their advantages over traditional ML. Develop best-practice workflows using capabilities like in-context learning (ICL) and benchmark rigorously against existing approaches.
Customer Success: Work directly with users to understand their challenges and demonstrate TFM value through technical demos tied to real business objectives. Guide onboarding to deliver quick wins and translate user feedback into technical insights for our product team.
Community & Education: Design and deliver workshops, tutorials, and content that explains the tabular foundation model paradigm — how it differs from LLMs and traditional ML, and why it matters. Engage the data science community through Kaggle, GitHub, and public-facing work.
PhD or Master's in a quantitative field, plus 3+ years of experience building and deploying ML/AI in industry, competitive ML, or open-source.
Deep proficiency in Python and the data science ecosystem, with hands-on experience in PyTorch and strong software engineering practices
Ability to translate complex technical concepts into tangible value for both technical and non-technical audiences
Genuine curiosity about new model architectures and a drive to explore what they can do
Kaggle Grandmaster, Master, or Expert status
Experience in technical consulting, solutions engineering, MLOps, or developer advocacy
Contributions to open-source libraries or data science tooling
A portfolio of blog posts, talks, or projects that demonstrate strong technical communication
Offices in Freiburg, Berlin, San Francisco and NYC with flexibility to work across our locations
Competitive compensation package with meaningful equity (We compete with the world's biggest AI companies for talent)
Work with state-of-the-art ML architecture, substantial compute resources, and a world-class team
Annual company-wide offsites to bring the team together (last trip was to the Alps 🏔️)
30 days of paid vacation + public holidays
Comprehensive benefits including healthcare, transportation, and fitness
Support with relocation where needed
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, disabilities, or any other traits that make you who you are.
prior-labs