Research Engineer, Foundation Model at Prior Labs (Berlin, Germany)Research Engineer, Foundation Model

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 nature.com/s41586-024-08328-6 cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics oxcan.org to preventing train failures with Hitachi siliconangle.com 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+ priorlabs.ai/about with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter linkedin.com/, Noah Hollmann linkedin.com/, and Sauraj Gambhir linkedin.com/, 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 priorlabs.ai/priorlabs-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 Engineers are the science team. You'll design experiments, contribute to papers, and write the code that turns architectural ideas into trained models - the same people do the research and the engineering, which is why both are good. 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.

• 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

• 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

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 priorlabs.ai/recruiting-data-privacy page