ML Systems Engineer

Who We Are: Prior Labs is building foundation models for spreadsheets and databases - the backbone of science and business. While foundation models have transformed text and images, structured data has remained largely untouched. We're tackling this $100B+ opportunity to revolutionize how we approach scientific discovery, medical research, financial modeling, and business intelligence.

Our Momentum: We're the world-leading organization working on structured data, and we're accelerating fast. Our TabPFN v2 model, recently published in Nature, sets the new state-of-the-art for structured data. We've hit 2.5M+ downloads, 5,000+ GitHub stars, and growth is accelerating. We're now building the next generation of models that combine AI advancements with specialized architectures for structured data.

The Team: We're a small, research-first team, 20 permanent hires selected from 5,000+ applicants - building the next generation of foundation models for structured data. Led by the founders behind the TabPFN lineage, we bring together talent from Google, Apple, Amazon, Microsoft, G-Research, Jane Street, and CERN, among others. We're backed by heavyweight advisors including Bernhard Schölkopf and Turing Award winner Yann LeCun. Meet the team here.

What's Next: Backed by top-tier investors and leaders from Hugging Face, DeepMind, and Silo AI, we're scaling fast. This is the moment to join: help us shape the future of structured data AI. Read our manifesto.

You'll play a critical role in ensuring the reliability, efficiency, and performance of our models at scale. You'll work on designing robust evaluation systems, optimizing model performance, and ensuring our infrastructure can support cutting-edge AI development. You'll also lead initiatives around data collection, benchmarking, and systematic evaluation to drive continuous model improvement and support our open-source community.

This is a rare opportunity to:

• Contribute to the development of high-impact AI systems that are changing an industry.

• Design and implement large-scale model evaluation and benchmarking pipelines from the ground up.

• Drive continuous performance improvements through rigorous, automated testing.

• Join a world-class team at the perfect time: significant funding secured, strong early traction, and rapid scaling.

• Systems Engineering & Performance Optimization: Design, implement, and maintain scalable infrastructure to support large-scale model training and evaluation. Identify and solve complex bottlenecks in our training and inference pipelines for efficiency and speed.

• Model Evaluation & Benchmarking: Develop comprehensive evaluation frameworks to assess model performance, robustness, and reliability. Design and maintain benchmarking protocols to compare our models against industry standards and track progress over time. Collaborate on efforts to collect, curate, and manage the high-quality datasets that are essential for training and evaluating our models.

• Open Source & Developer Experience: Act as a key maintainer of our open-source packages (tabpfn, tabpfn-extensions). Improve the developer experience through excellent documentation, clean APIs, and a contribution process for our community.

• Collaborate on Hard Problems: Work closely with our ML researchers to translate deep technical challenges into well-designed, scalable software systems.

• Exceptional software engineering fundamentals and expert-level proficiency in Python, demonstrated through 5+ years of experience and an advanced degree (MS/PhD) in Computer Science or a related field.

• A proven track record of architecting and building complex, scalable software, preferably for data processing, automated testing, or distributed systems.

• Deep, practical knowledge of the modern ML ecosystem (PyTorch, scikit-learn, etc.) and a genuine interest in applying systems thinking to solve hard problems in AI.

• Core MLOps Concepts: Strong understanding of the entire machine learning lifecycle (MLLC) from data ingestion and preparation to model deployment, monitoring, and retraining. Familiarity with MLOps principles and best practices (e.g., reproducibility, versioning, automation, continuous integration/delivery for ML).

• Offices in San Francisco, NYC, Berlin, and Freiburg, with flexibility to work across our locations

• Competitive compensation package with meaningful equity

• 30 days of paid vacation + public holidays

• Comprehensive benefits including healthcare, transportation, and fitness

• Work with state-of-the-art ML architecture, substantial compute resources and with a world-class team

• We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box."

• We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disabilities, or any other traits that make you who you are.