[Hiring] AI Engineer @Nava Benefits

Role Description

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.
• 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.

Qualifications

• 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.
• Shipping LLM features to production and living with them: imperfect model behavior, latency, cost and the tradeoffs between them.
• Modern full-stack development in TypeScript and/or Python, integrating foundation-model APIs, tool calling and human review into an application.
• Practical evaluation and tracing habits: you check what the model did and learn from real use.
• Explaining your decisions to product, design and domain experts, and turning their problems into reusable capabilities.
• No specific framework, model provider or minimum audience size is required. We assess comparable work and your reasoning.

Requirements

• Our Stack: React, Node, TypeScript, Python, Postgres and AWS; multiple foundation-model providers; sandboxed execution environments for agents; Langfuse for tracing and evaluation.
• Coding agents are part of daily work here, and encouraged but not required in the interview.

Benefits

• Protected space to build the AI platform at a company whose products actually run on it, with your patterns multiplying every AI team’s output.
• Frontier problems (long-running agents, context management, model routing, token economics) with real users in healthcare, not demos.
• A small, senior team and a direct line to the leaders making product and platform decisions.
• A remote-first company with a mission to fix healthcare and the tooling to do the best work of your career.

How We Interview

• A 15-minute introduction with our recruiter.
• An approximately 15-minute AI conversation about your most notable LLM-based work, who used it and what you learned from them (voice or typing, camera off).
• Two interview days booked together:
• A 60-minute live coding session on Day 1, in your usual environment with any coding agent you like.
• A 60-minute design conversation and a 60-minute accomplishment discussion on Day 2. Day 2 depends on the Day 1 outcome.
• We send preparation notes ahead of each step. Same questions for every candidate; we score evidence of comparable work, not polish.

Working at Nava

As a remote-first company, Nava is committed to building a dynamic and inclusive culture where you have the autonomy to thrive. You’ll be supported by cutting-edge technology, a collaborative team, and a shared mission to revolutionize healthcare.

Please note: The benefits below apply to full-time (W2) employees and are not included for contractor roles.

Candidates from all backgrounds are encouraged to apply. We believe that solving America's healthcare problem requires leveraging America's greatest strength: our diversity. Healthcare affects everyone – and a team that includes people from all backgrounds and walks of life will be more effective at driving change than a homogeneous one. We are excited to build that kind of team at Nava.