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 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

The anticipated annual base salary for this position is up to $250,000. This range does not include any other compensation components or other benefits for which an individual may be eligible. The actual base salary offered depends on a variety of factors, which may include as applicable, the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job.