ML Infrastructure Engineer for Scalable Medical Imaging AI

Epsilon Health; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Job summary

Epsilon Health is seeking an ML infrastructure engineer in San Francisco to design and build core systems enabling scalable training of large models for medical imaging and research. You will own distributed training, RL infrastructure, and inference pipelines from experimentation to production, collaborating with researchers and backend teams.

Responsibilities include partnering with researchers, building high-throughput data loading, and shipping end-to-end systems for training, evaluation,

Qualifications

• 6+ years designing, building, and operating large-scale distributed systems.

Responsibilities

• Partner with researchers to understand workflows and future needs.
• Build distributed training infra for foundation models on large medical imaging data.
• Develop high-throughput data loading to keep GPUs saturated.
• Prototype ideas and ship production-ready code end-to-end.
• Contribute to production serving, deployment pipelines, and monitoring.
• Build RL training stack for online, multi-reward RL at scale.

Skills

Distributed systems

ML infrastructure

Python

PyTorch/JAX

Kubernetes

DeepSpeed/Megatron

Research workflows

Reinforcement learning

Tools

vLLM

TensorRT

Trition

Cloud infrastructure

Job description

Epsilon Health is seeking an ML infrastructure engineer in San Francisco to design and build core systems enabling scalable training of large models for medical imaging and research. You will own distributed training, RL infrastructure, and inference pipelines from experimentation to production, collaborating with researchers and backend teams.

Responsibilities include partnering with researchers, building high-throughput data loading, and shipping end-to-end systems for training, evaluation,