Research Scientist, Foundation Model
Freiburg, Germany · Berlin, Germany · New York City, NY · Freiburg · Berlin · New YorkResearch Scientist, Scientist$70k–$270kPosted Jan 15, 2025
Research Scientist, Foundation Model LocationFreiburg; Berlin; New YorkEmployment TypeFull timeLocation TypeOn-siteDepartmentEngineering & ScienceScienceWho we areFoundation 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.Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it.Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub 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 BostonGene.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, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar.What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join.About The RoleTabular 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 scaleBuilding multimodal models that combine tabular, text, and numerical understandingMaking models efficient enough for real-world deployment — not just accurate enough for a paperDesigning architectures for time series, forecasting, anomaly detection, and multiple related tablesResearching causal understanding in foundation modelsWhat We're Looking ForPhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a closely related field, or equivalent research experience with demonstrated impactPublications at top-tier ML venues (NeurIPS, ICML, ICLR, etc.) or equivalent impact through widely used open-source, benchmarks, or deployed systemsStrong experience building and analyzing machine learning models, including transformer or other sequence-based architectures, using PyTorchSolid understanding of training dynamics, generalization, scaling...