- Build modular, generalizable, and portable ML training systems to support protein design models.
- Improve the scalability, reliability, and performance of ML training and inference infrastructure.
- Optimize model performance using GPU profiling, custom kernels, and accelerated computing frameworks.
- Develop and standardize agentic AI workflows to increase research velocity.
- Partner with AI scientists and protein engineers to translate research prototypes into robust systems.
- Contribute across the ML engineering stack from modeling to multi-node orchestration.
DockerKubernetesMachine Learning+2 more