ABOUT THE OPPORTUNITY
This is a rare opportunity to join a mission-driven health technology company at a pivotal stage of growth. The organization is building critical data infrastructure that sits at the intersection of real-world evidence, patient outcomes, and AI-enabled healthcare — working with hospitals, life sciences organizations, and a global network of clinical partners.
As a Staff Software Engineer, you won't just be shipping features. You'll be shaping the technical foundation of a platform that directly influences how healthcare decisions are made at scale. The work is complex, the data is sensitive, and the stakes are real. If you've been looking for a role where engineering craftsmanship genuinely matters — and where what you build has a measurable human impact — this is it.
The position is fully remote and open globally to candidates with strong communication skills in English (required).
PROJECT & CONTEXT
The engineering team is tackling some of the hardest problems in health data: harmonizing fragmented datasets, enabling privacy-preserving data sharing, and building reliable pipelines that power longitudinal patient research and real-world evidence generation.
You'll operate across a modern, evolving stack — Python 3.x, Django, TypeScript, JavaScript, React, Node.js, Kafka, and cloud infrastructure (AWS/GCP/Azure) — and will be expected to lead across the full lifecycle: architecture, design, implementation, review, observability, and production ownership.
AI-assisted development is embedded in the team's workflow. Engineers are expected to use coding agents, copilots, and AI-assisted tooling as standard practice — with full accountability for what they ship. Generated code is reviewed, tested, secured, and understood. If you can't maintain it, you don't merge it.
WHAT WE'RE LOOKING FOR (Required)
- 8+ years of professional software engineering experience, with a track record of building and operating production systems at scale
- Strong full-stack or backend-leaning engineering fundamentals — you design, write, review, and debug production-quality code
- Deep experience with modern web application stacks; familiarity with Python/Django, TypeScript/React, Node.js and related tooling
- Deep fluency with AI-assisted software development — you use coding agents and copilots as part of your everyday workflow, not as a novelty
- Demonstrated ability to guide, constrain, and review AI-generated code: setting context, defining acceptance criteria, keeping diffs reviewable, catching plausible-but-wrong output
- Experience applying AI tools across the software lifecycle: exploration, planning, implementation, refactoring, test generation, documentation, debugging, and operational investigation
- Proven experience leading complex technical initiatives across teams, systems, or product areas
- Strong system design skills: APIs, distributed systems, data flows, integrations, reliability, observability, performance, and security
- Experience with large or evolving codebases where maintainability, migration paths, and incremental delivery are first-class concerns
- Strong product judgment — you connect technical decisions to user value, customer needs, and business outcomes
- High agency in ambiguous environments — you create clarity, make sensible trade-offs, and move work forward without waiting for perfect requirements
- Strong ownership of production outcomes — you care deeply about how systems behave after they ship
- Excellent written and verbal communication in English — you can explain complex technical trade-offs to engineers, product managers, and leadership alike
- Collaborative, low-ego working style — you help teams make better decisions without introducing unnecessary process
NICE TO HAVE (Preferred)
- Experience in healthcare, health technology, life sciences, clinical data, real-world evidence, or another regulated enterprise domain
- Background with data-intensive platforms, interoperability, healthcare integrations, analytics products, or privacy-preserving data systems
- Experience building or deploying AI/LLM-enabled workflows, agents, evaluation systems, or production automation
- Experience shaping AI-assisted engineering practices at team level: repo instructions, agent workflows, review standards, test expectations, context management, or safe automation frameworks
- Hands-on experience with AWS, GCP, or Azure cloud platforms
- Experience with event-driven architectures, Kafka, workflow systems, data pipelines, or distributed processing
- Familiarity with security, privacy, compliance, and enterprise IT constraints in regulated environments
- A track record of turning recurring engineering problems into reusable platforms, tooling, or documentation
- Experience mentoring senior engineers and shaping technical culture across teams
