Research Scientist Intern (PhD)

Prior Labs is building foundation models that understand tabular data, the backbone of science and business. Foundation models have transformed text and images, but structured data has remained largely untouched. We’re tackling this $600B opportunity to fundamentally change how organizations work with scientific, medical, financial, and business data.

Momentum: We’re the world-leading organization in structured data ML. Our TabPFN v2 model was published in Nature and set a new state-of-the-art for tabular machine learning. Since its release, we’ve scaled model capabilities more than 20x, reached 2.5M+ downloads, 5,500+ GitHub stars, and are seeing accelerating adoption across research and industry. We’re now building the next generation of tabular foundation models and actively commercializing them with global enterprises across Europe and the US.

Our team: We’re a small, highly selective team of 20+ engineers and researchers, selected from over 5,000 applicants, with backgrounds spanning Google, Apple, Amazon, Microsoft, G-Research, Jane Street, Goldman Sachs, and CERN, led by the creators of TabPFN and advised by world-leading AI researchers such as 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 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

• 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

• 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

• 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

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. Diversity doesn’t just make us stronger, it also makes Prior Labs a more fun place to work.