Senior Staff Engineer, Developer Infrastructure & Experience

Hippocratic AI • Palo Alto, CA

3d • Onsite

About The Position

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.
This is a senior individual contributor role in Engineering, reporting to the VP of Engineering. You’ll set direction across the full software delivery lifecycle — testing architecture, build and release pipelines, feature flag strategy, observability, and developer tooling — partnering directly with ML researchers, clinical advisors, and product leaders to turn ambiguous requirements into production systems robust enough for healthcare contexts.
From establishing the design patterns that keep a fast-growing codebase comprehensible and testable by humans, to building the deployment infrastructure that makes frequent, safe releases possible, your work will determine how Hippocratic AI ships software for years to come.
We’re looking for senior staff-level engineers who have already built and scaled software systems across multiple technology generations — people who identify the structural problems others haven’t seen yet, set the technical bar for an entire organization, and bring others along with them.

Requirements

• BS in Computer Science or equivalent
• 12+ years of software engineering experience with production ownership at scale across multiple organizations
• Proven track record across multiple technology generations with clear, transferable judgment.
• Deep backend fluency in Python, Go, or Java; comfortable across the full stack.
• Strong command of software design: modularity, testability, separation of concerns, dependency inversion.
• Hands-on CI/CD ownership — building, operating, and improving pipelines in production.
• Production experience with feature flags, canary deployments, blue/green, and progressive rollouts.
• Demonstrated impact improving developer experience and internal tooling.
• Fluency with AI-assisted development tools — Copilot, LLM-based code generation, automated testing.
• Strong communicator across engineering, product, and clinical stakeholders.
• Committed to software safety, engineering rigor, and patient-centered outcomes.

Nice To Haves

• Experience in healthcare, life sciences, or a regulated, safety-critical industry.
• Familiarity with ML infrastructure, model serving, or LLM evaluation pipelines.
• Experience building engineering culture at a high-growth startup.
• Background in platform engineering or DevEx.
• Hands-on observability experience: structured logging, distributed tracing, SLO design.

Responsibilities

• 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 incident response.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: High school or GED