ML Infrastructure Engineer: Scalable Training and Deployment
Epsilon
Epsilon Health in San Francisco is seeking an ML infrastructure engineer to design core systems for scalable training of large models in medical imaging. You will enable researchers to run experiments efficiently, focusing on science rather than bottlenecks. In this role you own distributed training, data loading, inference and deployment pipelines, and the RL training stack; collaborate with research and backend teams to ship production-grade solutions. #J-18808-Ljbffr
Vacancy posted 14 hours ago
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