Technical Product Manager, Integrations

Who we are 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. The role Most enterprises will never call our API directly. They will meet TabPFN inside the platforms they already run: SAP, AWS, Azure, Google Cloud, Databricks, or their own cloud. How well that works, how fast each release arrives there, and which platforms come next is the job of our Technical Product Man [... source excerpt omitted ...] e founders, and you report to the CTO. This suits someone who enjoys landing things on a date as much as deciding what to build, and who still likes being close to the code. What you'll do Take every model release live across SAP, the cloud marketplaces and customer environments, on a plan you build with product and engineering. Be the technical counterpart for the partner teams that host TabPFN, from SAP's platform teams to the hyperscaler marketplaces. Shape how TabPFN shows up inside SAP's stack and what the integration engineering team builds first. Bring the open channel decisions to the founders with a recommendation, and stay hands-on: listings, docs, smoke tests, a [... source excerpt omitted ...] ide. You have deployed machine learning models and know what GPUs cost. You have worked at a startup, and you use AI tools to take real work off your plate. Life at Prior Labs 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