About Fleet 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
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’re looking for a Research Engineer to build reusable and reliable systems for synthetic data and evaluations.
This work spans synthetic data and task generation, verifiers, quality measurement, and the systems that connect and orchestrate the entire loop. You will develop new methods, test them on open-ended research problems, and turn the ones that work into systems that you and other researchers can use and extend.
You will work closely with internal research teams and frontier labs, study model behavior and traces directly, and build feedback loops between research findings and system improvements.
Success means making high-quality research faster, more reproducible, and easier to scale across researchers and domains, with the longer-term goal of systems that use evaluation feedback to recursively improve their own outputs.
What You'll Work On
Build reusable and reliable systems for producing high-quality synthetic data, tasks, and verifiers across software engineering, cyber, legal, finance, healthcare, and other technical and knowledge work domains.
Leverage research signals and expert knowledge to define and enforce quality standards for each eval set—understand what “good” looks like for each domain and iterate until it is right.
Build agentic hill-climbing and feedback loops that continuously improve the systems and their outputs against those standards. …
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