Applied Scientist, Data Science

Who we are

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 will join our data science team working at the frontier of tabular foundation models. As an Applied Data Scientist you sit between our tabular foundation models and the hardest problems customers bring to them. You take real customer data, an insurer's claims history, millions of card transactions, or proteomic blood panels for early cancer detection, and drive what TabPFN can unlock. You guide teams from the first discovery call through demos, POCs, and hands-on support all the way to production. Along the way you compare solutions against strong baselines, push results further with new workflows, and uncover strengths and weaknesses, feeding what you learn back to the model team and shape the next generation of TabPFN.

Guide customers to success with TabPFN. Own engagements end to end: discovery calls to understand the problem, technical demos tied to real business objectives, onboarding that delivers quick wins, and hands-on support as teams move from POC to production. Present results and capabilities so they land, a POC result that gets the green light, a demo that makes the value obvious. You will be in the room with their data scientists and their decision-makers, and you can hold your own with both.

Prove the model on their hardest problems. Run projects on real customer data: choose the right framing, build the evaluation harness, benchmark rigorously against strong incumbents, and report results honestly. When numbers look off, get to the root cause rather than tuning past it.

Push beyond the defaults. Where out-of-the-box is not enough, unlock more; fine-tuning, feature engineering, ensembling, in-context learning strategies, calibration, interpretability. Turn one-off wins into reusable workflows and tooling that every future engagement builds on.

Shape what we build next. You are the highest-signal channel between what customers actually hit and what we ship. Turn field learnings into reproducible findings, benchmarks, and roadmap input, for the next generation of TabPFN, and for the products and tooling that make it usable in the real world..

• 3+ years of hands on experience with ML/AI in industry, competitive ML, or open-source.

• Strong proficiency in Python and the modern data science ecosystem, with hands-on experience training and deploying deep learning models in PyTorch, including modern deep learning - architectures (especially transformers)

• Ability to translate complex technical concepts into tangible value for both technical and non-technical audiences

• Strong customer-facing skills, with the ability to independently drive technical conversations and engagements across pre-sales, POCs, and post-sales.

• Strong problem-solving skills, with the ability to quickly understand unfamiliar customer problems and translate them into practical data science/ML solutions.

• Broad ML knowledge and the ability to quickly adapt to new domains and problems.

• Strong communication and collaboration skills.

• Master’s or PhD in a quantitative field.

• Kaggle Grandmaster, Master, or Expert status

• Experience in technical consulting, solutions engineering or forward-deployed roles

• Experience with PyTorch, transformers, tabular data, or other modern ML approaches.

• Contributions to open-source projects, technical writing, talks, or workshops.

• SAP RSUs (Publicly traded, liquid once vested)

• 20 days paid vacation

• Health, dental and vision 100% employer-paid

• Fitness and transportation allowances

• 401k matching (2%, one-year cliff)

• Opportunity to publish your work

• Team offsites at least once a year

• Visa and relocation support (if required)

Life at Prior Labs

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.

Our Commitments

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