ML Infrastructure Engineer at Epsilon Health

Department: Engineering

Setup: On-site
Location: San Francisco, California
Level: senior
Posted: Sep 10, 2026

Description

Epsilon Health is hiring an ML infrastructure engineer to design and build distributed training, reinforcement learning, inference, evaluation, and production deployment systems for large foundation models in medical imaging and diagnostics. The role partners with researchers to translate experimentation workflows into production-ready systems, owns GPU-efficient training infrastructure, and contributes to model rollout and monitoring pipelines. Candidates need at least six years of experience with large-scale distributed systems or infrastructure, two years of ML infrastructure experience, strong Python and PyTorch or JAX skills, and deep Kubernetes and cloud infrastructure experience.

Required Skills

Python
PyTorch
Kubernetes
Distributed training
Reinforcement learning
Data loading

Additional Information

Required Previous Experiences: 6+ years designing, building, and operating large-scale distributed systems or infrastructure in production
2+ years building ML infrastructure or systems in production
Deep experience building and operating Kubernetes and cloud infrastructure at scale
Experience with distributed training at scale and large GPU job efficiency
Prior experience as a technical lead or mentor for other engineers
Preferred Qualifications: Experience operating in a startup or startup-like environment
Experience building reinforcement learning training infrastructure
Experience with high-performance inference and serving
Experience optimizing inference and serving for large models
Experience optimizing training performance
Experience building internal training or experimentation platforms
Familiarity with vision-language models or multimodal architectures