Software Engineer, ML Systems

Join Prior Labs!

Who We Are: Prior Labs is building breakthrough foundation models that understand spreadsheets and databases—the backbone of science and business. Foundation models have transformed text and images, but 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 Impact: We aim to be the world-leading organization working on structured data. Our TabPFN v2 model, recently published in Nature, sets the new state-of-the-art for small structured data. Our models have gained significant traction with 1M+ downloads and 3,500+ GitHub stars. We are now building the next generation of models that combine AI advancements with specialized architectures for structured data.

Backing and Momentum: With €9M in pre-seed funding from top-tier investors including Balderton Capital, XTX Ventures, and Hector Foundation—and support from leaders at Hugging Face, DeepMind, and Silo AI—we’re moving rapidly toward commercialization.

Read more about our vision on our blog.

About the role

As a Software Engineer for ML Systems & Evaluation, 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.

Key Responsibilities

• 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.

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

• Data Collection & Curation: Collaborate on efforts to collect, curate, and manage the high-quality datasets that are essential for training and evaluating our models.

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

Qualifications

• Exceptional software engineering fundamentals and expert-level proficiency in Python, demonstrated through 5+ years of experience or 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.

• A history of meaningful contributions to major open-source projects and/or a PhD or research background in a relevant field (distributed systems, databases, ML

Benefits

• 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