ML Engineer, Open Source

We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with.

We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you.

We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together.

Our Commitments

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, disability, or any other trait that makes you who you are.

We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.

• 3+ years building and maintaining Python packages or ML libraries used by others (open source track record strongly preferred)
• Deep fluency in PyTorch, scikit-learn, pandas, NumPy - their internals, extension points, and failure modes, not just their APIs
• Strong software engineering: testing, CI/CD, packaging (pyproject.toml, uv), semantic versioning, multi-version Python support
• Comfortable reading and working with model code - forward passes, checkpoint loading, inference optimization - and forming opinions about it
• Solid ML fundamentals: enough to write correct preprocessing, catch data leakage, and push back on design choices that break downstream
• Genuine care about developer experience: you write great docs and great error messages because you think they're engineering, not chores

Bonus:

• Maintainer or significant contributor to a popular open source ML/data library
• Strong AI tooling skills - you use Claude Code, Cursor, or similar fluently to move fast
• MCP server or tool-use integration experience
• HuggingFace Hub model distribution experience
• Background in tabular data, AutoML, or time series
• Experience debugging cross-platform packaging, or contributing to PyTorch/sklearn core

Foundation 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., Most companies treat open source as a side job for researchers who'd rather be doing something else. We think that's wrong. Prior Labs is rooted in open source - TabPFN started as a research project the community adopted, and that's how we became a company. Language models and image models have had years to build out their ecosystem interfaces and integrations. For tabular foundation models, none of that exists yet. You're not plugging into existing patterns - you're creating them. The engineering is genuinely hard: TabPFN does in-context learning, not traditional fit/predict, so wrapping it behind a clean sklearn interface means solving problems no other library has solved. You're designing APIs for a model whose architecture evolves faster than users can upgrade, and making inference robust to the full chaos of real-world tabular data. You understand the model deeply enough to push back when something will break downstream, and you care enough about the details to write great docs and error messages on top of great code. What you'll work on: * Design sklearn-compatible APIs around a foundation model that doesn't behave like a traditional estimator - solve the hard abstraction problems so the interface feels simple * Build and maintain PyTorch serialization, HuggingFace Hub model distribution, and checkpoint management across a multi-model, multi-version ecosystem * Build MCP and tool-use wrappers for agentic AI pipelines * Model-adjacent ML engineering: preprocessing pipelines, inference wrappers, dtype handling, edge case hardening against real-world data * Own releases, CI, testing, and docs across the TabPFN ecosystem - TabPFN (core), tabpfn-client, tabpfn-extensions, tabpfn-time-series * General ML engineering: benchmarking, evaluation pipelines, data loading, tooling that makes the team faster