RESEARCHER, AGENTS FOR AUTOMATED DISCOVERY
ABOUT THE COMPANY
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small, well-funded team that works onsite in San Francisco.
ABOUT THE ROLE
You'll be researching the agents at the core of our work: multi-agent systems that conduct automated machine learning research and discovery. You'll design how these agents plan, decompose problems, choose what to try next, evaluate their own outputs, and recover from mistakes.
This is a deeply open-ended research role. The benchmarks for agents that do real research don't exist yet, and inventing them is part of the job. You'll move between method design, careful experimentation, building evaluation frameworks, and shipping into production. Real autonomy, real ownership, and the corresponding responsibility for choosing well.
WHAT YOU'LL DO
Design methods that improve how our agents plan, decompose tasks, use tools, manage context, and recover from failures across long-horizon research workflows
Develop multi-agent coordination
