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Founding Member of Technical Staff — RL

Naïve

Mountain View, CAFull-timeSeen todayStill listed today

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At a glance

Compensation
No compensation found
Location
Mountain View, CA
Schedule
Full-time
Work Authorization
Not specified

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Job overview

Naive builds autonomous companies using agent systems that operate real businesses end‑to‑end, releasing templates, benchmarks, and a studio for deployment. The founding member will help create agent environments, design tasks and rewards, and develop production‑ready blueprints while contributing to research and performance improvements.

Skills & qualifications

RequiredNice to have

Skills

PythonLLMsAI AgentsResearch Engineering AbilityAgent DevelopmentEvals DevelopmentRL EnvironmentsTool UseLong‑Horizon TasksLLM Failure ModesExperiment DesignProduction SystemsHigh AgencyRL ExperiencePost‑Training ExperienceBrowser Agent ExperienceCoding Agent ExperienceComputer‑Use Agent ExperienceBenchmark PublishingTechnical ResearchDistributed Agent Infrastructure

Full job description

About Naive Naive is building autonomous companies: agent systems capable of operating real businesses end to end. We release autonomous company templates and benchmarks, alongside a studio—where users can deploy and operate these systems. Our infrastructure platform, Vetta, powers long-running, high-volume agent workloads. We’ve raised $28.5M from Nexus Venture Partners, Y Combinator, Zetta Venture Partners, Liquid 2 and leading operators. The Role Build real-world agent environments, benchmarks and autonomous company blueprints—and make Vetta the best-performing agent within them. Accordingly, as a founding MTS member, you have the chance to earn significant equity in a fast growing Series A company. What You’ll Do

  • Turn complex business workflows into reproducible agent environments
  • Design tasks, rewards, evals and benchmarks that measure real outcomes
  • Build production-ready autonomous company blueprints
  • Run experiments, analyze failures and improve agent performance
  • Develop training data and optimization loops from agent trajectories
  • Publish credible benchmarks, technical reports and demos

Must-Haves

  • Strong Python and research-engineering ability
  • Experience building agents, evals or RL environments
  • Deep understanding of tool use, long-horizon tasks and LLM failure modes
  • Ability to design rigorous experiments and ship production systems
  • High agency and comfort working on ambiguous 0→1 problems

Nice-to-Haves

  • RL or post-training experience
  • Browser, coding or computer-use agent experience
  • Experience publishing benchmarks or technical research
  • Familiarity with distributed agent infrastructure

P.S. If you’ve made it to the bottom of this listing and are serious about every point on this role, send Sean a LinkedIn connect with a note.

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