Software Engineer - ML Infrastructure

Epsilon; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

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.

Qualifications

• 6+ years designing, building, and operating large-scale distributed systems in production.
• 2+ years building ML infrastructure or systems in production.
• Strong Python skills and expertise in PyTorch or JAX.
• Experience and familiarity with the compute, tooling, and workflow needs of large-scale ML research.
• Experience building infrastructure or platforms specifically for research or ML workflows.
• Deep experience building and operating Kubernetes and cloud infrastructure at scale.
• Experience with distributed training at scale (FSDP, DeepSpeed, or Megatron-style parallelism).
• Prior experience as a technical lead or mentor for other engineers.

Responsibilities

• Partner directly with researchers to understand workflows and design needs.
• Build a distributed training infrastructure for foundation models on large-scale medical imaging.
• Build high-throughput data loading and preprocessing to keep GPUs saturated.
• Prototype ideas and translate them into production-ready code with end-to-end delivery.
• Contribute to production serving and deployment pipelines alongside the backend team.
• Build the reinforcement learning training stack for online, multi-reward RL at scale.

Skills

Distributed systems

ML infrastructure

Python

PyTorch

JAX

Kubernetes

Cloud infrastructure

Large-scale training

Leadership

Research workflows

Tools

DeepSpeed

Megatron

TensorRT

Triton

vLLM

Job description

About Us

We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.

Role Overview

We’re looking for an ML infrastructure engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Epsilon Health fast and reliable to ensure our research teams can focus on science rather than system bottlenecks.

Sitting in the Engineering team and working closely with research, you'll own the distributed training and reinforcement learning infrastructure our foundation-model and post-training work runs on, and the inference and evaluation systems that carry models from experimentation into production.

Key Responsibilities

• Partner directly with researchers to deeply understand their workflows, then anticipate and design for how those needs will change

• Build a distributed training infrastructure for foundation models on large-scale medical imaging, including the long-context parallelism and checkpointing that volumetric CT/MR training demands.

• Build high-throughput data loading and preprocessing that keeps GPUs saturated on large volumetric and multimodal datasets.

• Partner with researchers to prototype new ideas and translate them into production-ready code, owning end-to-end delivery from experimentation through deployment and monitoring.

• Contribute to production serving and deployment pipelines (model rollout, canary deployments, and monitoring) alongside the backend team.

• Build the reinforcement learning training stack (high-throughput rollout generation, reward-model serving, and experience collection), enabling the research team to run online, multi-reward RL at scale.

Qualifications

• 6+ years of experience designing, building, and operating large-scale distributed systems or infrastructure in production

• Have 2+ years of experience building ML infrastructure or systems in production

• Strong Python skills and expertise in PyTorch or JAX

• Experience and familiarity with the compute, tooling, and workflow needs of large-scale machine learning research

• Experience building infrastructure or platforms specifically for research or machine learning workflows

• Deep experience building and operating Kubernetes and cloud infrastructure at scale

• Experience with distributed training at scale (FSDP, DeepSpeed, or Megatron-style parallelism) and the systems concerns of keeping large GPU jobs efficient

• Prior experience as a technical lead or mentor for other engineers

Preferred Qualifications

• Experience operating in a startup or startup-like environment, i.e. a small, fast-moving team with high autonomy

• Experience building reinforcement learning training infrastructure: rollout generation, reward-model serving, or online/off-policy learning systems

• Experience with high-performance inference and serving (vLLM, SGLang, TensorRT, or Triton) for both training-time rollouts and production

• Experience optimizing inference and serving for large models: batching, KV/prompt caching, quantization, and low-latency, high-throughput sampling.

  • Experience optimizing training performance: parallelism, distributed communication, mixed/low precision, and utilization.

• Experience building internal training or experimentation platforms used by research teams, supporting A/B testing and experimentation workflows

• Familiarity with vision-language models (VLMs) or multimodal architectures