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.
About the role
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 enterprise apps to YouTube videos to Kaggle 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 …
Official source: fleetai.com. Details can change; the platform's page is the final word. How we check listings.
