- Location
- San Francisco Bay Area
- Employment
- Full time
- Level
- Mid level
- Posted
- 28 October
About this role
About Fleet
Fleet studies how environments produce intelligence: the environment determines what capabilities develop, what behaviors survive, and what "good" looks like. We build the benchmarks that expose where frontier models break and the training that closes the gap. Backed by Sequoia Capital, Menlo Ventures, BCV, and SV Angel.
About the Role
We're looking for a Research Scientist to work on scalable oversight for agents: detecting misbehavior like reward hacking and deception, understanding why it happens, and keeping verification reliable as models outgrow the humans and automated systems checking them. Because Fleet builds sandboxed environments and training tasks for agents, our task factory will be your controlled laboratory where you can intervene, attribute behavior to its cause, and feed what you learn back into improvements. You'll own research threads that ship as papers, benchmarks, training gyms, models optimized for efficient oversight, or production systems, in direct collaboration with frontier labs.
What You'll Work On
- Scalable oversight: methods that keep verification reliable as tasks get longer and models get stronger.
- Monitoring: detecting reward hacking or undesirable behavior patterns in our agent traces.
- Failure mode analysis and attribution: tracing agents' mistakes and misbehaviors upstream to root causes in data, environments, or model weights.
- Turning oversight into training signal: better verifiers, process supervision, and models trained to catch what humans miss.
What We're Looking For
- Publication record at top venues (NeurIPS, ICML, ICLR, or equivalent), or equivalent shipped research, in oversight, monitoring, evaluations, alignment, interpretability, AI control, or RL.
- Strong empirical instincts; comfortable with LLM APIs, harnesses, and large trace datasets.
- High ownership: you run your own research threads and are measured by what you ship.
Location
San Francisco (on-site).
Compensation
Salary range: $200,000 - $600,000 USD annually, plus meaningful equity.
As published by Fleet AI. Applications are handled on their site.
Skills this posting mentions
About Fleet AI
Gym Environments for Frontier Agents
All 12 openings at Fleet AIOne click, then it is written
Apply to Fleet AI with a resume written for this role.
Queue Research Scientist, Oversight and I read the posting, rewrite your resume against it, draft the cover letter, and score the fit. Then you press send, or press one button and I fill in Fleet AI’s form for you.
01Drop your resume
A PDF or a LinkedIn URL. About a minute, once.
02I rank the openings
Every weekday morning, the live catalog scored against your history. Up to 20 worth your time, not two hundred links.
03Each one is written up
Resume rewritten for the posting, a cover letter, a fit score. Press send, or let me fill in the form.
- 25 sent a week, free
- No card
- Nothing sent until you say so
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Put this to work
Paste your career in once. Every application after that is written for you.
Drop a resume or a LinkedIn URL. I rank the live openings against it, rewrite the resume and write a cover letter for the best of them, and fill in the employer's form when you press the button. You read, you decide what goes out.
01Drop your resume
A PDF or a LinkedIn URL. About a minute, once.
02I rank the openings
Every weekday morning, the live catalog scored against your history. Up to 20 worth your time, not two hundred links.
03Each one is written up
Resume rewritten for the posting, a cover letter, a fit score. Press send, or let me fill in the form.
- New matches ranked and written before you are up.
- Every bullet stays inside what your history supports. Nothing invented.
- Queued, submitted, interviewing, offer: one screen, not a spreadsheet.
Free, no card. Up to 25 applications sent a week, and nothing is sent until you say so.
Listed from the job board Fleet AI publishes. Refolk is not the employer and does not handle their hiring. Applications go to Fleet AI directly.
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