Platform

Fleet AI is an applied artificial intelligence company working on human-AI collaboration at scale, backed by Sequoia Capital, Menlo Ventures, BCV, and SV Angel. Compute can be bought and text can be scraped. But agents only learn real jobs by doing them, which takes a world where the job exists and the outcome can be graded. That is what we build. Our training gyms are high-fidelity simulations of how real businesses actually run. Agents practice genuine work inside them while people supervise, evaluate and steer their behavior. Each one is a step toward an allocation economy, a future where humans direct work rather than perform it.

About the Role

Platform is where Fleet’s leverage compounds. Every environment we ship and every training run we grade sits on systems this team owns. A week saved here is a week saved for every engineer and researcher in the company, on every environment, from then on. When the platform is good, one person ships what used to take a team.

The team is accountable for the agent runtime and the execution layer every environment runs on. It also owns the durable record of every run and the production infrastructure the rest of engineering depends on.

What You’ll Do

• Own the agent runtime. Make a long agent rollout a first-class object rather than a process. It should pause, resume at the exact step it stopped, and stay steerable while it is still running.
• Make environments ship in one motion. Carry an environment from a repository to a graded deployment along a single dependable path, so that shipping the hundredth one feels like shipping the first.
• Design the systems of record. Give every task, trajectory and verifier one durable home. The contract should be clear enough that other teams can build against it without asking you first.
• Make long jobs boring. Multi-hour work that checkpoints cleanly, retries honestly, and can tell you exactly where it is at any moment.
• Put numbers on model and harness choices. Build the evaluation that shows what a change does to quality, latency and cost, so those tradeoffs get argued from data.
• Turn one team’s solution into everyone’s capability. When something works well for Research or Environment Engineering or Deployments, make it a platform primitive the whole company inherits.

What We’re Looking For

• Strong backend and systems fundamentals, including production experience keeping something distributed or asynchronous alive
• Range across infrastructure and interface, from designing a queue one day to building the internal tool for inspecting it the next
• Judgment about when a shared abstraction earns its keep and when a targeted fix is the more honest answer
• A habit of instrumenting first, so you can say what a change did to reliability, to cost, or to somebody’s afternoon
• Comfort chasing a failure across service boundaries and carrying the fix all the way into production
• Fluency with AI coding agents as part of how you work

• Full-time and on-site in San Francisco or New York, with an extremely competitive salary and meaningful equity.

How you build and think matters more to us than credentials.