Join Prior Labs!

PriorLabs is building breakthrough foundation models that understand spreadsheets and databases - the lifeblood of science and business. While foundation models have transformed text and images, tabular data has remained largely untouched. We're tackling this $100B+ opportunity with technology that could revolutionize how we approach scientific discovery, medical research, financial modeling, and business intelligence.

Our initial release TabPFN achieved state-of-the-art performance with significant traction (100K+ downloads, 1,200+ GitHub stars), and we're now building even more powerful models that combine the latest AI advances with specialized architectures for tabular data.

Led by Frank Hutter, the most cited researcher in AutoML, our team of world-class AI researchers and engineers has a track record of pioneering breakthroughs. With substantial backing from top-tier investors, we're rapidly moving toward commercialization. Want to help organizations worldwide unlock the true value of their most critical data assets? Join us in making it happen.

Core Areas of Impact

You'll be among the first scientists collaborating and working an entirely new class of AI models, not just incremental improvements. As an early-stage startup working on foundation models for tabular data, we have countless exciting research ideas and problems to explore - you're sure to find challenges that match your interests and expertise. We are working on problems such as:

• Scaling our transformer architectures from 10K to 1M+ samples while maintaining performance
• Building multimodal models that combine text and tabular understanding on proprietary data
• 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

What We're Looking For

• Currently pursuing or holding a PhD in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related field (we will also consider exceptional Master's students)
• 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

Benefits

• Strong mentorship and professional development opportunities