ML Engineer, Backend

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. ABOUT THE ROLE You will have ownership over designing, building, and scaling the core systems that bring Prior Labs' foundation models to the world. This is a unique opportunity to make fundamental architectural decisions, establish engineering best practices from the ground up, and profoundly shape the technical direction for serving state-of-the-art AI. TabPFN does its learning in the forward pass, and it now handles datasets in the millions of rows. Making that fast, reliable, and affordable is an exciting engineering challenge. You'll work directly with world-class AI researchers, translating cutting-edge models into scalable production systems. This role offers significant autonomy and impact, with clear paths to specialize in areas you're passionate about (like inference optimization and core backend systems) or grow into a technical leadership position as our team expands. You won't just be implementing features; you'll be building the backbone of our company. What you'll work on: - Own Inference: Own the serving path end-to-end: latency, throughput, batching, memory behavior on large inputs, and cost per prediction. Our models serve through our managed API, inside customer VPCs, and as self-hosted open weights, and you’ll have an impact across the board. - Architect & Design: Design robust, scalable, and secure backend systems and production-grade APIs for serving our foundation models. - Build & Implement: Develop high-quality, maintainable code for core backend services. - Own Infrastructure: Design, deploy, and manage core infrastructure on cloud platforms, focusing on reliability, monitoring, observability, and cost-efficiency. - Ensure Compliance & Security: Implement secure, GDPR-compliant systems, including data storage, access control, usage tracking, and quota management. - Champion Best Practices: Drive high standards for testing, CI/CD, documentation, and security within the engineering team. You may be a good fit if you have: - 3+ years of professional experience in machine learning and backend engineering, with a proven track record of managing production infrastructure. - Proven experience deploying and operating machine learning models in production, with a strong understanding of how models behave and fail under real traffic. - Strong, hands-on experience designing and building production-grade APIs and backend services. - Significant…