Research Scientist – RL Post-Training for Agents

Want your RL research to land in agents that run for days in the real world, not in a paper appendix? A brand-new AI lab in San Francisco is building autonomous agents that pursue complex goals over very long horizons. The founding team comes from leading frontier AI labs, autonomous-driving and robotics AI, big-tech research and a top quant firm. This is a research seat that builds real systems: you own ambitious bets from the first hypothesis and dataset all the way to a deployed capability. They’re hiring 4 research scientists. What you’ll own Using RL, and whatever else works, to post-train LLM-based and multimodal agents Long-horizon capabilities: planning, memory, error recovery, proactivity, self-improvement, and knowing when to escape to a human Building the environments where agents use computers and tools, plus the data pipelines, benchmarks and evals around them Ways for agents to grasp what a user wants and stay true to it over long runs Careful experiment design, and taking what works all the way into production A real say in the research agenda from day one What you bring Exceptional ML research and engineering skills Depth in at least one of these: RL, LLM post-training, reasoning, agents, computer use, long-horizon systems, memory and context, evals, or how humans and agents work together Good judgment on what to test and when to stop. You build complete systems and move easily between ideas, large-scale experiments and production. You’ve led something significant: a model, an agent system, a benchmark, a paper, an open-source project or a big research bet Roughly 3–6 years in; frontier-lab experience valued, exceptional outliers and senior leads welcome High agency and comfort with uncertain directions