ML Engineer, Open Source

ML Engineer, Open Source LocationBerlin; Freiburg; New YorkEmployment TypeFull timeLocation TypeOn-siteDepartmentEngineering & ScienceEngineeringWho 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 roleMost 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 simpleBuild and maintain PyTorch serialization, HuggingFace Hub model distribution, and checkpoint management across a multi-model, multi-version ecosystemBuild MCP and tool-use wrappers for agentic AI pipelinesModel-adjacent ML engineering: preprocessing pipelines, inference wrappers, dtype handling, edge case hardening against real-world dataOwn releases, CI, testing, and docs across the TabPFN ecosystem — TabPFN (core), tabpfn-client, tabpfn-extensions, tabpfn-time-seriesGeneral ML engineering: benchmarking, evaluation pipelines,...