About the role

Who We Are Nava is on a mission to #fixhealthcare. Nearly 160M Americans rely on their employers for healthcare — yet the system is broken, bloated, and dominated by incumbents who resist change. Nava fuses deep benefits expertise with cutting-edge technology to deliver a modern, transparent, and affordable healthcare experience. Founded by seasoned entrepreneurs and backed by leading investors, Nava is one of the fastest-growing benefits brokerages in the country. We’re the first to combine brokerage know-how with proprietary tech like HQ: our AI-powered benefits platform, designed to help HR teams take control of renewals, simplify strategy, eliminate spreadsheet chaos, and empower employees to navigate their benefits with ease. In a $50B industry hungry for change, Nava is built to win — lowering costs for employers, delighting employees, and reshaping how healthcare works for millions of Americans. About This Role & Why It Matters We’re hiring an AI Engineer to build and harden the platform underneath Nava’s LLM-based products. Nava sits at the bleeding edge of AI-driven products, and building there has taught us something: what comes out of the box for LLM applications is not enough. Agents run long-running tasks, which changes how we deploy software and manage state. Context has to be shipped into zero-trust sandboxes on different platforms, then retrieved and refreshed on our own terms. Different models need to serve different jobs at a cost we can sustain as usage grows quickly. To ship our products, we built first versions of all of this ourselves, each for the use case in front of us. What makes this opportunity unique: you take those first versions and harden them into a platform that every Nava AI team builds on. This is protected work, not a ticket queue. It exists to reduce the unplanned platform work landing on the product engineers at the tip of the spear, and to create the magic that lets the rest of the company move faster with AI. It is LLM-based product engineering at the frontier of current practice, focused on the patterns for building and maintaining an AI ecosystem, with real employers, employees and Nava teams on the other end. What You’ll Accomplish in Your First Year - Harden the platform for reuse: Take the first-version capabilities built for one product (context management, agent state, sandboxed execution, model routing) and make each reusable across use cases beyond the one it was built for, so new AI features start from the platform instead of rebuilding it. - Make long-running agents safe to run: Design the state management and deployment patterns so an in-flight agent task survives deploys, restarts and retries, and can be resumed, inspected and replayed. - Own context in and out of the sandbox: Build one thoughtful mechanism for packaging context into zero-trust execution environments across platforms, and for ejecting and retrieving new context as an agent works. - Bring cost per task down as we scale: Route the right model to the right job, orchestrate subagents so expensive models do only expensive work, and make token usage measurable and optimized as volume grows. - Give product engineers their time back: Measurably reduce the unplanned platform work interrupting the AI product teams, so their sprints go to features rather than plumbing. - Make the ecosystem maintainable: Document, test, trace and evaluate the shared patterns so a new engineer can adopt them in days and regressions are caught before users notice. What You’ll Bring We understand that your experience is more than just a list of requirements, so we encourage you to apply even if you don't meet all of the following bullet points. - Hands-on ownership of the infrastructure under an LLM-based product that other engineers or users depended on: an agent runtime, context or state management, orchestration, or evaluation and tracing. Internal users and inherited systems count. - Shipping LLM feature