Member of Technical Staff, Environment Generation

About Fleet AI

Fleet studies how environments produce intelligence. We believe intelligence is an emergent property of environmental pressures: the environment determines what capabilities develop, what behaviors survive, and what "good" looks like.

We work with frontier labs on post-training across modalities, building benchmarks that expose where frontier models break, training recipes that close those gaps, and scalable oversight for long-horizon agents. Backed by Sequoia Capital, Menlo Ventures, BCV, and SV Angel.

About the Role

We are looking for engineers to build code generation pipelines that produce high quality, high fidelity RL environments at scale.

We come at this from two directions:

• Terraforming the internet: In the same way Google indexed the web, we crawl it ourselves, looking for environments that teach agents useful behavior. Every part of the digital world, from apps to videos to online competitions to prediction markets, can become a pipeline that builds rich simulations for agent training.
• Learning from agent experience. While we use up the internet, a new data source is growing exponentially. Agents are working in the real world and producing huge volumes of experience. We want to learn from it with open-loop simulation (as is common in self-driving), synthesize environments from it, and turn the real world into a verification signal.

We obsess over quality and realism. A bug in an environment teaches the agent the wrong behavior, and a bug in our generator repeats it across many environments.

There are a lot of unknowns, so you will be expected to propose and evaluate different approaches.

What You'll Work On

• Building and scaling agentic systems that generate large, complex software with minimal human input
• Making those systems reliable over long running work, and debugging them when they aren't
• Holding a very high bar for quality and correctness in everything they produce
• Prototyping and evaluating new approaches to problems nobody has solved yet

What We're Looking For

• Prior experience with code generation and agent harnesses
• You've worked on coding agents that do long, complex work reliably
• You understand coding agent failure modes very well
• You are comfortable working on nebulous problems
• You care a lot about quality and know how your work can break
• Bonus:
• Experience across the end to end SDLC
• You've built automated systems that judge the quality of complex software and exhaustively find bugs

How We Work

• Small, technical team.
• Quick proofs of concept to find good approaches.
• Speed with rigor.
• Truth over comfort. Honest with partners, with each other, and with ourselves.
• On-site in SF or NYC.

Compensation

Salary range: $200,000 - $600,000 USD annually, plus meaningful equity.