Research Engineer, ML Infrastructure
Required skills
- AI/ML
- model training
- model evaluation
- GPU clusters
- parallelism
- quantization
- custom kernels
- bottlenecks
- failures
- throughput
Nice to have
- frontier models
- biochemical structure
- molecular interaction
- computational workspace
- molecule iteration
- therapeutic engineering
Role summary
Chai Discovery is seeking a Research Engineer specializing in ML Infrastructure to enhance the performance, efficiency, and reliability of their AI models for drug discovery. This role involves building and optimizing the model training stack, profiling large GPU clusters, eliminating bottlenecks, and ensuring the fault tolerance and deterministic orchestration of long-running jobs. The ideal candidate possesses strong software system design skills, proficiency in Python, and fluency in PyTorch or JAX, with a track record of exceptional impact on real-world ML systems.
About Chai Discovery
Chai Discovery builds the computer-aided design suite for molecules. Eli Lilly, GSK, Pfizer, Bristol Myers Squibb, argenx and Novartis 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
Make our models performant, resource efficient and reliable at scale by developing the core frameworks for model training and evaluation, in close partnerships with fellow researchers and engineers.
Build our training stack across model, layer, and kernel levels; optimize workloads through parallelism, quantization, and custom kernels.
Profile end-to-end training runs on large GPU clusters; eliminate bottlenecks and failures; monitor throughput, utilization, and uptime.
Ensure new model architectures and training recipes scale efficiently, from early experiments to frontier-scale runs.
Make our ML training stack maximally reliable: fault tolerance, checkpointing, and deterministic orchestration for long-running, large-scale jobs.
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 and systems 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 and promote a culture of high velocity and ownership. We compensate our team accordingly.
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