ML Engineer
Prior Labs
ML Engineer
Prior Labs
Develops tabular foundation models for data
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 cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. 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+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, 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 as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.
A model that tops a benchmark and a model that changes how an organization works are two different things. You'll take our tabular foundation models into our most strategic customers' environments - integrating them into real platforms and pipelines, doing the hands-on data science to prove value, and owning the implementation through to production.
This is a senior role: You'll own engagements end-to-end with high autonomy, make hard technical calls under ambiguity, and work shoulder-to-shoulder with customer data science teams as a senior peer. The patterns you uncover in the field won't stay in the field - they flow straight back to our researchers and shape what we build next. You won't just deploy models; you'll help shape how Prior Labs deploys as we scale.
What You'll Do:
Integrate: Embed our foundation models into customer platforms, cloud environments, and ML pipelines.
Do the Data Science: Frame the problem on real, messy data, engineer features, model it, and benchmark rigorously against the customer's current baseline.
Own Delivery: Carry use cases end-to-end - from first conversation to a reliable, documented production solution you stand behind.
Partner Deeply: Work with customer data science and ML teams as a peer, earn their trust, and make them faster with our models.
Tailor & Optimize: Customize models for diverse use cases, trading off performance, latency, scale, and cost.
Close the Loop: Turn deployment insights into sharp, prioritized feedback that shapes the model and product roadmap.
Set the Standard: Establish the deployment patterns the team builds on as we scale.
3+ years building and deploying ML systems in production, with a track record of owning hard problems end-to-end.
Strong engineering fundamentals and expert-level Python.
Deep, hands-on ML ability - you build models you understand and can defend, not just call an API. Strong with PyTorch and scikit-learn, with a solid grasp of transformer / foundation-model approaches.
Depth in tabular, time series, or structured-data ML.
Proven cloud deployment (AWS, GCP, or Azure) into production, kept reliable under real-world conditions.
Mature customer instinct - you diagnose the real problem, navigate technical and business stakeholders, and drive to outcomes.
High autonomy and sound judgment in ambiguity, and a bias toward clean, maintainable, well-documented code.
What sets you apart
Prior forward-deployed, solutions engineering, or senior technical customer-facing experience.
Contributions to relevant open-source projects in ML or data engineering.
Experience integrating with enterprise data ecosystems and designing APIs and deployment pipelines.
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 page
Develops tabular foundation models for data
Prior Labs builds TabPFN-style tabular foundation models (TFMs) that operate on structured enterprise and scientific data. Their models can make predictions directly from tabular data without task-specific training (no per-dataset fine-tuning), enabling tasks like churn prediction, supplier risk assessment, and demand forecasting from single tables. The company differentiates itself by offering open-source TFMs with a focus on tabular data, active collaboration with industry and research, and a clear commitment to openness even after acquisition by SAP. Their goal is to provide practical, scalable AI for tables that can be deployed across business processes and scientific workflows, while continuing open-source development and long-term frontier research.
Explore more opportunities at Prior Labs