
Founding Member of Technical Staff — RL
Mountain View, CA at a glance
- Rent
- #2 of 51$2,780/mo+46% vs US avg
- Weather
- #17 of 51311 mild days0 hot · 0 cold
- Income tax
- #1 of 5113.3% top rateCalifornia
What you need
- Strong Python and research-engineering ability
- Experience building agents, evals or RL environments
- Deep understanding of tool use, long-horizon tasks, LLM failure modes
- Ability to design rigorous experiments and ship production systems
- High agency and comfort with ambiguous 0→1 problems
What you'll do
- Turn complex business workflows into reproducible agent environments
- Design tasks, rewards, evals and benchmarks measuring real outcomes
- Build production-ready autonomous company blueprints
- Run experiments, analyze failures, improve agent performance
- Develop training data and optimization loops from agent trajectories
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.
Interview Process
About the interview
- Screening call with Co-founder (CEO or CTO)
- Technical Interview (Project-based, no leetcode)
- Work trial offer / Full time conversion
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