Foundation models have transformed text and images, but structured data - the largest and most consequential data modality in the world - has remained untouched. Tables power every clinical trial, every financial model, every scientific experiment, every business decision. No one has built a foundation model that truly understands them.
Until now. What LLMs did for language, we're doing for tables.
Momentum: We pioneered tabular foundation models and are now the world-leading organization in structured data ML. Our TabPFN v2 model was published in Nature and set a new state-of-the-art for tabular machine learning. Since its release, we've scaled model capabilities more than 20x, reached 3M+ downloads, 6,000+ 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 in front of us. We're scaling tabular foundation models to handle millions of rows, thousands of features, real-time inference, and entirely new data modalities - while building the infrastructure to deploy 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 20+ engineers and researchers, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G-Research, Jane Street, Goldman Sachs, and CERN, led by Frank Hutter, Noah Hollmann and Sauraj Gambhir and advised by world-leading AI researchers such as Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, create 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 modality shift in AI is happening - and we're hiring the team that makes it.
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 Engineers aren't supporting scientists — they are the science team. You'll design experiments, contribute to papers, and write the code that turns architectural ideas into trained models. We create cutting edge research 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
Day-to-day, you'll design and test novel architectures, run ablations, analyze scaling behavior, and write the training and evaluation infrastructure that makes rapid experimentation possible. We hold software quality to the same standard as research quality.
What We're Looking For
Master's or PhD in Computer Science or a related field, plus 3+ years of experience building ML systems in research or industry
Publications at top ML venues (NeurIPS, ICML, ICLR, etc.) or equivalent demonstrated research impact (widely used open-source, deployed systems)
Deep proficiency in Python, PyTorch, and the broader ML and data science ecosystem (scikit-learn, pandas, NumPy), with strong software engineering practices
Experience implementing and training neural network architectures — ideally transformers or foundation models
Solid understanding of training dynamics, scaling behavior, and common failure modes in deep learning systems
Genuine interest in model efficiency — making large models faster, more scalable, and practical to deploy
Nice to Have
Experience at an early-stage startup or as a founding engineer
Contributions to open-source ML libraries or tools
Experience with model distillation, inference optimization, or on-device ML
Background in tabular data, time series, or other structured data — helpful but not required
Offices in Freiburg, Berlin, San Francisco and NYC, with flexibility to work across our locations
Competitive compensation package with meaningful equity (We compete with the world's biggest AI companies for talent)
Work with state-of-the-art ML architecture, substantial compute resources, and a world-class team
Annual company-wide offsites to bring the team together (last trip was to the Alps 🏔️)
30 days of paid vacation + public holidays
Comprehensive benefits including healthcare, transportation, and fitness
Support with relocation where needed
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, disabilities, or any other traits that make you who you are.
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