Research Scientist, Foundation Model

About The Role

Tabular data breaks the assumptions that make scaling work for language and vision. There's no natural sequence, no spatial structure, no shared vocabulary across datasets. The architectures and scaling laws that power LLMs don't transfer. We've made the first breakthrough with TabPFN — but the hardest problems are still ahead.

At Prior Labs, Research Scientists drive the core model agenda. You'll define research directions, design novel architectures, and publish work that advances the field — while ensuring your ideas translate into models that actually ship. We create cutting-edge models because the same people do both. As an early team member, you'll have significant technical ownership and room to grow as we scale.

The problems we're solving:

Scaling transformer architectures from 10K to 1M+ samples — without the structural assumptions that make language models scale

Building multimodal models that combine tabular, text, and numerical understanding

Making models efficient enough for real-world deployment — not just accurate enough for a paper

Designing architectures for time series, forecasting, anomaly detection, and multiple related tables

Researching causal understanding in foundation models

What We're Looking For

PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a closely related field, or equivalent research experience with demonstrated impact

Publications at top-tier ML venues (NeurIPS, ICML, ICLR, etc.) or equivalent impact through widely used open-source, benchmarks, or deployed systems

Strong experience building and analyzing machine learning models, including transformer or other sequence-based architectures, using PyTorch

Solid understanding of training dynamics, generalization, scaling behavior, and common failure modes in deep learning systems

Excellent engineering fundamentals and strong Python skills, with a track record of writing high-quality research code

Nice to Have

Experience at an early-stage startup or research lab with a shipping culture

Contributions to open-source ML libraries or tools

Experience with model distillation, inference optimization, or efficient architectures

Background in tabular data, time series, or other structured data — helpful but not required

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 TabPFN, 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 offsites 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.