About the job
Join Our Team
At Prior Labs, we are revolutionizing the way organizations interact with tabular data—the essential framework of scientific and business operations. While foundation models have already transformed text and image processing, structured data has largely been overlooked. We are seizing a $600B opportunity to redefine how industries manage their scientific, medical, financial, and business information.
Our Achievements: As the leading authority in structured data machine learning, our TabPFN v2 model has set a new benchmark presented in Nature. With over 2.5 million downloads and more than 5,500 stars on GitHub, our model capabilities have grown by over 20x, showcasing its rapid adoption across both research and commercial sectors. We are now developing the next generation of tabular foundation models for global enterprises in Europe and the US.
About Our Team: Comprising over 20 elite engineers and researchers selected from a pool of more than 5,000 candidates, our team boasts expertise from industry giants such as Google, Apple, Amazon, and CERN. Led by the creators of TabPFN and advised by eminent AI researchers like Bernhard Schölkopf and Turing Award winner Yann LeCun, we are poised for groundbreaking advancements. Meet the team here.
Looking Ahead: With backing from top-tier investors and industry leaders from Hugging Face, DeepMind, and Silo AI, we are experiencing rapid growth. This is an exciting time to join us as we shape the future of structured data AI. Explore our manifesto.
Role Overview
As a pioneering Research Scientist, you will be at the forefront of developing a groundbreaking class of AI models. Our latest innovation, TabPFN, has significantly outperformed existing methodologies, marking a pivotal moment in AI research. This role offers a unique chance to:
Engage in transformative AI breakthroughs rather than merely incremental advancements.
Influence global organizational practices regarding their most valuable data.
Join us at a crucial juncture following our recent funding and rapid scaling.
We are challenging the limits of transformer architectures in structured data. Key challenges include...

