Staff Research Scientist

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 2,500+ 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.

About the Role

You'll be among the first scientists developing an entirely new class of AI models. Our latest breakthrough (TabPFN) outperforms all existing approaches by orders of magnitude - and we're just getting started. This is a rare opportunity to:

• Work on fundamental breakthroughs in AI, not just incremental improvements

• Shape the future of how organizations worldwide work with their most valuable data

• Join at the perfect time: We just received significant funding, have strong early traction, and are scaling rapidly

We're pushing the boundaries of what's possible with transformer architectures for structured data. Key challenges include:

• Scaling our transformer architectures from 10K to 1M+ samples while maintaining performance

• Building multimodal models that combine text and tabular understanding

• Developing specialized architectures for time series, forecasting, and anomaly detection

• Creating efficient inference methods for production deployment

• Researching causal understanding in foundation models

• Designing novel approaches for handling multiple related tables

Qualifications

• 5+ years industry experience in Machine Learning, AI or related field

• PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related field

• Deep experience with ML frameworks, especially PyTorch and scikit-learn

• Strong engineering fundamentals with excellent Python expertise

• Experience in data-science and working with tabular data or time series

• Publications at top-tier venues (NeurIPS, ICML, ICLR) or significant open-source contributions

Location

• Offices in Freiburg, Germany - a university city at the edge of the Black Forest, Switzerland and France, and Berlin—a global tech hub and one of Europe’s most dynamic cities

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