Staff Software Engineer, AI-Native Systems
Wheel · USA
About this role
**Staff Software Engineer, AI-Native Systems (Tech Lead)** **Location:** Remote (US) --- ## About the role Wheel builds the infrastructure behind virtual care—systems that let telehealth run at scale for patients, clinicians, and companies. Instead of bolting AI onto old workflows, we’re rebuilding from the ground up with intelligent, agent-powered systems that carry real operational load in a live, regulated healthcare environment. You’ll set technical direction for agentic systems and lead the work to ship them. You’ve built and operated agents in production, know where they break, and can make load-bearing architectural calls (including when the answer is “not yet” or “buy, don’t build”). You operate with the scope of a domain owner and drive ambiguous problems to shipped, measured outcomes. --- ## What you’ll do ### Technical Leadership & Direction - Own technical direction for a significant AI-native domain (agent architecture, platform abstractions, or evaluation/guardrail infrastructure) - Act as tech lead for a squad or cross-team initiative: decompose ambiguity, sequence delivery, clear blockers, and keep focus on outcomes - Write and review design docs; establish clear technical ownership where it’s currently diffuse ### Agent Architecture & Engineering - Design and build production AI agents with retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability - Define standards for “production-ready agent” (testability, rollback safety, cost ceilings, failure modes, human-in-the-loop boundaries) - Take on the hardest parts of the build yourself (hands-on role) ### AI Platform Foundations - Build and extend abstraction layers so teams can integrate AI capabilities cleanly and safely across services - Define shared libraries/patterns/guardrails and drive adoption - Translate privacy, security, and compliance constraints into concrete architecture ### Cloud-Native Engineering - Own full-stack delivery in **TypeScript/Node.js** and **Python**: service/API layers, data-processing jobs, and internal interfaces - Use modern cloud infrastructure, event-driven patterns, CI/CD, and observability to deliver scalable AI-native systems - Own deployment, monitoring, troubleshooting, and on-call; improve operational posture ### Stakeholder Engagement & Advisory - Partner with product, operations, clinical operations, and business leaders to define which use cases are worth building - Lead design sessions, proofs of concept, and build-with sessions to build trust and adoption - Communicate trade-offs, risks, and recommendations clearly to technical and non-technical audiences (including executives) ### Measure & Improve - Own evaluation strategy for your domain: metrics, test harnesses, and evaluation plans (accuracy, latency, safety, cost) - Instrument systems so behavior is legible after the fact (not just at demo time) - Iterate quickly on data/feedback; kill approaches that aren’t working early and visibly ### Grow the Org - Mentor and grow engineers through code review, design review, pairing, and direct feedback - Create reusable patterns, documentation, and best practices that raise the bar beyond your team - Help anchor an internal community of practice around AI-native and agentic engineering --- ## What success looks like - **First 90 days:** working map of AI platform surface area, shipped something real, and a point of view on where leverage is - **First 6 months:*
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