Research Scientist, Foundational 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.

Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them.

Momentum

We pioneered tabular foundation models and are now the world-leading organization in structured-data ML.

Our Tab PFN v2 model was published as a Nature cover story and set a new state‑of‑the‑art for tabular machine learning.

Since its release we have scaled model capabilities 20×+, passed 3.5 M+ downloads and 7 500+ Git Hub 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 Boston Gene.

The Hardest Work Is Ahead

We’re scaling tabular foundation models to millions of rows, thousands of features, real‑time inference, and entirely new data modalities, while building the infrastructure to run 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 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, Deep Mind, Meta, Microsoft Research, G‑Research, Jane Street, Goldman Sachs, and CERN.

We are led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world‑leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann Le Cun.

We ship fast, do top‑tier research, and hold each other to an extremely high bar.

What’s Next

In 2025 we raised €9 m pre‑seed led by Balderton Capital, backed by leaders from Hugging Face, Deep Mind, and Black Forest Labs.

The next phase of growth is here, making this an ideal time to join.

• What You’ll Do
• Invent and build frontier tools that extend Tab PFN, including its thinking, scaling, and agentic capabilities, and the new methods that let one model generalise across the full landscape of data‑science problems.

This is the most open‑ended part of the work and grows over time.

• Set the research direction by deciding which model capabilities and benchmarks are worth pursuing, choosing what is worth solving rather than optimising a score someone else set.
• Bring in external research and real customer needs to shape new model and tooling directions, and publish frontier results that move the field forward.
• Build trustworthy benchmarks from the structured data behind real, high‑impact problems, so the team optimises for real‑world performance rather than one leaderboard.
• Faithfully implement the baselines and competitor models that set the gold standard of applied data science, giving the team a read on where Tab PFN leads and where there is room to improve.
• Build an automated, agentic pipeline with a human in the loop so this data and benchmark foundation scales to far larger volumes without losing rigor, itself a genuinely new tool.
• What We’re Looking For
• You have solved data‑science problems across many domains and datasets to a high standard, optimising for strong performance across a whole suite of tasks rather than the single best score on one.
• You work undogmatically across the ML toolbox, including getting strong results with gradient‑boosted trees (such as XGBoost) and not only with deep learning.
• You understand the common categories of dataset defects (leakage, label noise, distribution shift, duplication, mislabelled targets, and similar) and why each corrupts a training or benchmark signal.
• You are energized by foundational work, valuing the dataset and benchmark bedrock as much as the frontier tooling, and you have taken on hard problems others passed over.
• You thrive as a senior individual contributor in an ambiguous, early‑stage, low‑process environment.

You are opinionated on best practice in Data Science and can make good judgement calls on approaches to complex problems.

• Nice to Have
• Experience building or extending evaluation harnesses, benchmark suites, or experiment frameworks that others rely on.
• Experience building LLM‑ or agent‑assisted pipelines with a human in the loop to scale a previously manual workflow.
• Experience acting as the link between external research or customer needs and an internal model or product roadmap.
• Prior work on tabular, structured‑data, or foundation‑model problems, or helping shape an emerging research subfield through community work.
• 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.

We’re building our teams in Berlin, Freiburg, and New York and we believe that when you’re working on something as hard and exciting as Tab PFN, being in the same room matters.

Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we’re open to remote.

This usually involves frequent travel to one of our offices and the whole company comes together regularly for off‑sites to think, build, and celebrate together.

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.

#J-18808-Ljbffr