Research Scientist, Pre-training Research

ABOUT CHAI DISCOVERY

Chai Discovery builds the computer-aided design suite for molecules. Eli Lilly https://endpoints.news/eli-lilly-chai-discovery-sign-ai-software-deal/, GSK https://endpoints.news/chai-discovery-signs-gsk-deal-to-make-biologics-with-ai/, Pfizer https://www.forbes.com/sites/amyfeldman/2026/06/04/why-pfizer-and-eli-lilly-are-betting-on-this-13-billion-ai-drug-discovery-startup/, Bristol Myers Squibb https://www.linkedin.

com/feed/update/urn

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activity:7496300911523246080/, argenx https://x.com/chaidiscovery/status/2077422480617267230?s=20 and Novartis https://www.chaidiscovery.com/news/novartis-partnership all use Chai to power drug discovery programs. AI is reinventing life sciences the same way it reinvented software engineering, and our team is building that future.

We train frontier models that learn the underlying foundations of biochemical structure and interaction. We pair this with a computational workspace to iterate on molecules from idea to medicine, helping scientists move faster and develop medicines beyond the reach of existing methods.

We value diverse perspectives and are ready to find greatness in unexpected places.

ABOUT THE ROLE

We are hiring research scientists to work on foundation model pre-training. Our models learn the foundations of molecular structure and interaction, and their success depends on carefully designed architectural components, training objectives, and data mixes.

At Chai, pre-training research is largely domain-agnostic

understanding model architecture, reading training curves, and knowing when to move between phases of a recipe. We are biased toward simplification, generality, and scale. We adapt the best ideas from frontier language, image, and video models into our domain.

Our models have moved beyond protein structure prediction into real-world therapeutic engineering, and you will have the compute and the team to push that further.

ABOUT YOU

We look for researchers with a strong track record, which can take many forms: getting a model to work, developing impactful theory, or making a substantial contribution to the field.

In particular we look for

• Depth in generative modelling, model architecture, or scaling laws.
• Hands-on experience designing and training large models.
• Theoretical grounding in CS, ML, math, or physics.

We are happy to bet on rising talent.

WE OFFER

The opportunity to work at the vanguard of AI research and frontier biology, with world-class people, on a mission that matters. We protect and promote a culture of high velocity and ownership. We compensate our team accordingly.