Senior Staff Engineer, Developer Infrastructure & Experience

Job Overview

As a Senior Staff Software Engineer at Hippocratic AI, you’ll define the engineering standards, CI/CD infrastructure, and developer platform that let a safety-critical healthcare AI company ship with confidence. You’ll architect the foundational systems that power reliable, testable, and incrementally deployable software across every product and partner deployment — operating at the intersection of software craftsmanship, production engineering discipline, and AI-first development workflows.

Why Join Our Team

Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare-only, safety-focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.

Work with the people shaping the future. Hippocratic AI was co-founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.

Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others.

Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world-changing technologies — ensuring our platform is powerful, trusted, and truly transformative.

Location Requirement

We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Palo Alto office five days a week, unless otherwise specified.

About the Role

As a Senior Staff Software Engineer at Hippocratic AI, you’ll define the engineering standards, CI/CD infrastructure, and developer platform that let a safety-critical healthcare AI company ship with confidence. You’ll architect the foundational systems that power reliable, testable, and incrementally deployable software across every product and partner deployment — operating at the intersection of software craftsmanship, production engineering discipline, and AI-first development workflows.

What You’ll Do

• Define the engineering standards and quality bar across the organization. Set and uphold best practices for code structure, testing architecture, observability, and deployment. Lead by example in code reviews and system design discussions in ways that raise the floor for every engineer around you.


• Own production readiness end-to-end. Build and ship features where testability, observability, incremental deployability, and safe rollback are design constraints from day one — not afterthoughts. Define what ‘done’ means across the engineering org and enforce it without becoming a bottleneck.


• Architect for testability and modularity at scale. Bring structural discipline to a codebase that must grow quickly without rotting. Champion clean interfaces, dependency inversion, and appropriate layering — the design patterns that make complex, AI-powered production systems comprehensible and testable by humans.


• Own the end-to-end CI/CD pipeline and release infrastructure. Design and implement staged rollout strategies — canary deployments, blue/green releases, progressive delivery — that give the team confidence to ship frequently without gambling on reliability in a clinical environment.


• Establish and steward feature flag and safe deployment practices. Build the infrastructure for gradual rollouts, controlled experimentation, and fast recovery. Make incremental delivery the default across the engineering org, not the exception.


• Multiply developer velocity through systematic tooling improvements. Identify and eliminate friction across the development lifecycle — local environments, test infrastructure, build speeds, linting, and inner-loop performance. Make the team measurably faster without accumulating new technical debt.


• Operate fluently in an AI-assisted development environment. Use AI-powered coding tools, automated test generation, and LLM-assisted debugging as force multipliers. Help the broader team adopt these workflows thoughtfully — capturing the speed gains while maintaining the engineering rigor a safety-critical platform demands.


• Serve as the technical voice across Clinical, ML, and Product. Translate between ML researchers, clinical advisors, and product managers. Turn ambiguous, cross-functional requirements into systems that are robust, maintainable, and safe to operate in healthcare contexts.


• Treat patient safety as a first-class engineering concern. Our software runs in clinical environments where reliability directly impacts patient outcomes. Bring that gravity — not as a constraint on velocity, but as a design principle — to every architectural decision, deployment practice, and infrastructure choice.