Research Scientist Intern (PhD)

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.

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

• Work with state-of-the-art ML architecture and substantial compute resources

• Shape the future of data science and AI

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