Staff Software Engineer, AI-Native Systems
Staff engineer tech lead designing and building AI-native agent systems (retrieval, orchestration, tool calling) for a telehealth infrastructure platform using TypeScript/Node.js and Python in a cloud-native, remote US setting.
- Compensation
- $185.7K–$264.5K / yr
- Location
- Remote, USA
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years building and operating production software
- Full-stack depth across TypeScript/Node.js backend
- At least one other language (Python strongly preferred)
- Track record of technical leadership as an individual contributor
- Hands-on experience designing and deploying agentic systems in production
- Experience with retrieval, orchestration, tool/function calling, and evaluation
- Ability to drive loosely defined problems to shipped, measured outcomes
- Working practice of using AI development tools as a force multiplier
- Cloud-native engineering fundamentals: CI/CD, observability, running what you build
- Fluency with relational data and SQL
- Clear written and verbal communication for a remote, cross-functional role
- Comfort with ambiguity and bias toward shipping measurable results
Nice to have
- Experience with agent frameworks and multi-agent architectures at production scale
- Model evaluation and guardrail infrastructure experience
- Building platform capabilities consumed by other engineering teams
- Background in workflow automation, forecasting-driven products, or supply-demand matching
- Prior work in a regulated environment (healthcare/HIPAA, fintech, etc.)
- Experience mentoring engineers or acting as a formal tech lead
Full description
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.