• Write and ship production code weekly—you'll be in the codebase alongside your team • Drive a culture of shipping speed and continuous improvement—identify bottlenecks, reduce cycle times, and treat fast iteration as the primary mechanism for achieving quality in a regulated environment • Build internal tools that give clinical and operations users visibility into AI decision-making, with appropriate guardrails, permissions, and correction workflows • Work closely with an embedded AI engineer who owns prediction model quality, while you own the production systems, user-facing workflows, and speed gains that depend on those predictions • Lead incident response and postmortems for team-owned systems; ensure failures result in durable fixes, not patches • Mentor 3 engineers through building together—pairing, code review, and architectural decisions made in the context of real work • Establish and maintain system reliability standards: monitoring, alerting, and continuous improvement loops that catch problems before users do