Applied Scientist, Data Science

Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables.

We pioneered tabular foundation models: TabPFN v2 was a Nature cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level.

We're a small, highly selective team of 40+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun.

In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.

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 training and deploying deep learning models in PyTorch, including modern deep learning - architectures (especially transformers)

• Collaborative development on GitHub 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

You'll work alongside researchers and builders who hold themselves to a very high bar - in the quality of their work and in how they work with each other. We move fast and still take the time to do things right.

Our teams are based in Berlin, Freiburg, and New York - when you're working on something as hard as TabPFN, being in the same room matters. But great people come from everywhere, and in exceptional cases we're open to remote, which usually means frequent travel to one of our offices. Wherever you're based, the whole company comes together regularly for offsites to build and celebrate together.

The best products and teams are built by people with a wide range of perspectives and backgrounds. We welcome applications from all identities and walks of life - especially if you've ever felt discouraged by "not checking every box" - and provide 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 - see our Recruiting Data Privacy page