Research Engineer

About Chai Discovery

Chai builds the design suite for molecules. We train frontier models that learn the underlying foundations of biochemical structure and interaction, so scientists can move faster and pursue targets that other methods cannot reach.

AI is reinventing life sciences the same way it reinvented software engineering, and Chai is at the forefront of this shift. Leading pharmaceutical companies like Eli Lilly, Pfizer, and Novartis are adopting our platform to power their drug discovery programs.

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

About the role

Make our models performant, fast, and reliable at scale by developing the core frameworks used to train and evaluate our ML models.

• Build datasets and algorithms to systematically evaluate and monitor model performance.

• Analyze model failure modes, working closely with researchers on experiments to mitigate them.

• Own the distributed training stack, removing runtime and reliability bottlenecks across all layers of the stack and architecture—parallelism, quantization, and kernel optimization and whatever else might move the needle.

• Own and contribute to our auto research and auto kernels frameworks.

Chai's models are moving beyond protein structure prediction into real-world therapeutic engineering. This is a chance to push the frontier of AI drug design, working alongside a rigorous and craft-obsessed team.

About you

Ideal backgrounds include deep industry experience working with top AI/ML teams on the kinds of problems we describe above—with strong software system design skills, proficiency in Python, and Pytorch or JAX fluency. We look for technical spikes where you have gone deep and demonstrated exceptional impact on real-world problems and systems.

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 & promote a culture of high velocity and ownership. We compensate our team accordingly.