About the role

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

You may be a good fit if you have:

• 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

Life at Prior Labs
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.