ML Engineer, Forward Deployed

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. CORE AREAS OF IMPACT 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. WHAT WE’RE LOOKING FOR: - 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…