Prior Labs · Los Angeles
Data & AIEngineering & ScienceFullTimeposted 20mo ago
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.
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
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
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
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