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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. Pre-training research is largely domain-agnostic, focusing on model architecture, training curves, and recipe phases. We prioritize simplification, generality, and scale, adapting frontier ideas from language, image, and video models. You will have the compute and team to push our models, which have moved beyond protein structure prediction into therapeutic engineering, further.
Responsibilities
- Work on foundation model pre-training.
- Design architectural components, training objectives, and data mixes for models learning molecular structure and interaction.
- Adapt best ideas from frontier language, image, and video models into our domain.
- Push the capabilities of models beyond protein structure prediction into real-world therapeutic engineering.
Requirements
- Strong track record, which can take many forms: getting a model to work, developing impactful theory, or making a substantial contribution to the field.
- Depth in generative modeling, model architecture, or scaling laws.
- Hands-on experience designing and training large models.
- Theoretical grounding in CS, ML, math, or physics.
- Willingness to bet on rising talent.
Skills
- Generative modeling
- Model architecture
- Scaling laws
- Designing large models
- Training large models
- Computational modeling
- AI research
- Frontier biology
Benefits
- Opportunity to work at the vanguard of AI research and frontier biology.
- Work with world-class people.
- Work on a mission that matters.
- Culture of high velocity and ownership.
- Competitive compensation.
About the Company
- Chai Discovery builds the computer-aided design suite for molecules.
- Eli Lilly, GSK, Pfizer, Bristol Myers Squibb, argenx and Novartis use Chai to power drug discovery programs.
- AI is reinventing life sciences.
- 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.