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 - 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 - the same people do the research and ship the models. 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 PyTorchSolid 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 LabsYou'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
