Prior Labs · Berlin
Skills: AWS
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(https://www.nature.com/articles/s41586-024-08328-6) cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics(https://www.oxcan.org/news/prior-labs-and-oxford-cancer-analytics-partner-to-advance-liquid-biopsy-and-clinical-decision-making-in-lung-disease) to preventing train failures with Hitachi(https://siliconangle.com/2025/12/01/prior-labs-debuts-tabular-ai-foundation-model-scales-10-million-rows/). 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+(https://priorlabs.ai/about) with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter(https://www.linkedin.com/in/frank-hutter-9190b24b/), Noah Hollmann(https://www.linkedin.com/in/noah-hollmann-668b9010b/), and Sauraj Gambhir(https://www.linkedin.com/in/sauraj-g/), 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(https://priorlabs.ai/blog-posts/priorlabs-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 Manager for Integrations.
You take each model release from handover to live acro…
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