Staff Software Engineer, AI-Native Systems

Staff Software Engineer, AI-Native Systems (Tech Lead) Location: Remote (US) We are Virtual care only works if the infrastructure behind it does. Wheel builds that infrastructure — the systems that let telehealth run at scale for the patients, clinicians, and companies depending on it. We're not bolting AI onto old workflows; we're rebuilding them from the ground up. Our mission to put great healthcare in everyone's reach means replacing manual, ad-hoc processes with intelligent, agent-powered systems that carry real operational load. We're innovating in a live, regulated healthcare environment where getting it right matters. This is the next architecture of virtual care, and we're building it now. You are A staff-level engineer who sets technical direction for agentic systems and then leads the work to ship them. You've built and operated agents in production, you know where they break, and you have opinions — held loosely, argued well — about how to build them so they hold up in a regulated environment. You operate with the scope of a domain owner, not a task owner. You take a problem that isn't yet well-formed, define it, sequence it, and lead a group of engineers to a shipped and measured outcome. Your impact shows up in other people's work as much as your own: the patterns they reuse, the design decisions they don't have to relitigate, the ambiguity you removed before it cost the team a quarter. You treat AI two ways at once — as a product capability you build with judgment, and as a development multiplier you use fluently — and you're the person who raises the org's bar on both. The Work Technical Leadership & Direction • Own the technical direction for a significant AI-native domain: agent architecture, platform abstractions, or evaluation and guardrail infrastructure. • Act as tech lead for a squad or a cross-team initiative — decomposing ambiguous problems, sequencing delivery, identifying and clearing blockers, and keeping the team pointed at the outcome rather than the ticket. • Write and review design docs; make and document the load-bearing architectural calls, including the ones where the answer is "not yet" or "buy, don't build." • Establish clear technical ownership where it's currently diffuse, so decisions have a named owner and reviews don't stall. Agent Architecture & Engineering • Design and build production AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability. • Set the standards for what "production-ready agent" means here — testability, rollback safety, cost ceilings, failure modes, human-in-the-loop boundaries — and hold the bar in review. • Take on the hardest parts of the build yourself. This is a hands-on role; you are expected to be in the code. AI Platform Foundations • Build and extend the abstraction layers that let teams integrate AI capabilities cleanly and safely across our services. • Define the shared libraries, patterns, and guardrails other teams build on, and drive their adoption — a pattern nobody uses isn't a pattern. • Treat responsible use of AI on sensitive data as a hard engineering requirement, and translate privacy, security, and compliance constraints into concrete architecture rather than deferring them. Cloud-Native Engineering • Own full-stack delivery in TypeScript/Node.js and Python: service and API layers, data-processing jobs, and the internal interfaces on top of them. • Leverage modern cloud infrastructure, event-driven patterns, CI/CD, and observability to deliver scalable AI-native systems. • Own deployment, monitoring, and troubleshooting in production, including on-call, and improve the operational posture of what you inherit. Stakeholder Engagement & Advisory