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Member of Technical Staff, Applied Research Scientist

Fleet
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Hybrid (San Francisco, CA / New York, NY)Full-timePosted 4mo ago

About the role

Applied Research Scientist role focused on designing robust environments, realistic data, and challenging tasks for evaluating frontier AI models. Acts as bridge between partner engagements and core research, delivering end-to-end artifacts and feeding insights back into core research to drive ongoing advancements.

Responsibilities

  • Understand the ask. Take a lab request and figure out what they actually need: not the literal spec, the underlying capability they're trying to teach or measure. Push back when the ask is wrong.
  • Ship environments, tasks, and data. Deliver an artifact that runs end-to-end and is ready to go into a training or eval run.
  • Be diligent. Know your failure modes. Your artifact will be used to train or evaluate a frontier model. A broken reward, a leaked answer, a degenerate solution path, a flaky environment can corrupt a multi-million-dollar run or invalidate a benchmark.
  • Close the loop. Each engagement teaches you something about how the lab's model fails, what reward shape actually trained, where an eval held up or didn't.
  • Compound the platform. Reusable harnesses, eval protocols, dataset patterns, and infra primitives you build for one delivery become the substrate for the next.

Qualifications

  • Strong engineer. Production Python; comfortable across containers, distributed systems, and a GPU when you have to.
  • Deep understanding of ML. Knowledge of how models learn from environments, rewards, RL training dynamics, supervision, preference, and RL data.

Skills

Required:Engineering proficiency (Python)Distributed systemsML understandingResearch tasteOwnershipAmbiguity tolerance
Last updated Oct 8, 2026. Interested? Apply on the company site.

About Fleet

Fleet - AI-powered workflows

Fleet creates simulated worlds and real-world challenges to better understand and shape the behavior of artificial intelligences.

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