About Us

Epsilon Health is transforming medical imaging and diagnostics by bringing frontier AI into clinical practice. We're tackling some of healthcare's most critical challenges, building technology that directly improves patient outcomes.

This year, we've served over 300,000 patients and now serve 3,000+ daily, working with some of the largest medical institutions in the U.S. By year-end, we're on track to interpret 1% of all daily U.S. X-rays.

Our research team brings experience from DeepMind and Meta, powered by one of the industry's most comprehensive proprietary medical imaging datasets, continuously enriched by real-world clinical data. Together, we're building a future where frontier AI helps save millions of lives today, not tomorrow.

Recently out of stealth, we're proudly backed by Jack Altman, Uncork Capital, Renegade Partners, and AlleyCorp, one of NYC's most active healthcare venture firms.

Role Overview

We're looking for Software Engineers to help us build and operate the core infrastructure that powers Epsilon Health, composed of 3 major components: an ingestion pipeline, a data warehouse / pipeline and a distributed inference system.

You'll work across the backend stack, designing services, evolving schemas, improving observability, and keeping production healthy. This isn't a role where you'll own a single microservice. We expect you to understand how the pieces fit together, make thoughtful architectural decisions, and leave the system simpler than you found it.

We don't have a separate SRE organization. Reliability, operations, debugging, and capacity planning are part of engineering here. As Epsilon Health grows, one of your most important jobs will be making sure the backend grows without becoming unnecessarily complicated.

You'll be working closely with the ML team, lending a hand on ML infrastructure when priorities demand, and you'll do well here if you like owning ambiguous, high-impact problems end to end.

Key Responsibilities

Design, build, and operate infrastructure that deliver reliable end-to-end experiences from data ingestion through model output.

Design and implement robust data pipelines and warehouses to collect, process, and store large-scale multimodal medical imaging data from both production traffic and offline sources.

Build CI/CD and deployment automation that lets a lean team ship quickly and safely.

Ensure systems handling PHI are HIPAA-compliant by design, with encryption, access controls, audit logging, and secure data handling throughout.

Build model inference pipelines for live and offline traffic, and support ML infrastructure alongside the ML team as needs arise.

Leading technical design, driving quality through design reviews and testing, and helping set engineering best practices on a lean team.

Qualifications

6+ years of experience building data infrastructure, storage systems, or related distributed systems

Have 2+ years of experience leading large scale, complex projects or teams as an engineer or tech lead

Proficiency in a backend language (Go, Python, Java, or similar)

Experience designing, building, and operating large-scale distributed systems or infrastructure (we use AWS) in production

Strong problem-solving skills and the ability to troubleshoot complex distributed systems

Can navigate complex technical tradeoffs between performance, cost, security, and maintainability

Track record of leading complex, multi-quarter technical initiatives spanning multiple teams or systems

Preferred Qualifications

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

Proficiency in modern web application technologies for the frontend (e.g., TypeScript, React) with a strong understanding of web architecture

Experience building and operating HIPAA-compliant systems, or handling PHI and healthcare security requirements

Experience with large-scale ETL, data lake, or data warehouse systems

Experience contributing to ML infrastructure or model inference pipelines