RL Environment Software Engineer

**Location:** San Francisco, CA **Work Model:** Onsite **Industry:** Applied AI / AI research data **Compensation:** $180K-$220K base, ~$400K+ OTE (uncapped profit share) ### About the Company Our partner is a fast-growing applied AI research lab that builds high-quality reinforcement-learning environments and agents sold to the world's leading AI labs. In under two years they have scaled to a nine-figure revenue run rate and grown their team severalfold in a matter of months, backed by leading venture investors. Quality is their core differentiator, and they are rapidly expanding into new domains. ### The Opportunity As an RL Environment Software Engineer, you will sit at the intersection of research engineering and traditional software engineering, building the environments that simulate real-world workflows and the agents that automate them. This is forward-looking work, you will help research and predict what high-quality environments the frontier will need next, then build them from the ground up. You will join a brand-new RL team being assembled with exceptional talent, with a clear path to grow alongside it as the function scales into industry pods. ### Responsibilities - Design and build high-quality RL environments that simulate real working environments end to end. - Develop agents for the tasks within those environments and iterate until they are efficient and production-ready. - Partner with the research team to scope which environments to build and why, staying ahead of future demand rather than only meeting present needs. - Own the backend and infrastructure layers that make environments reliable and scalable. - Help set engineering standards for a zero-to-one team as the RL function grows. ### Requirements - Strong machine-learning engineers who code heavily and build systems from scratch, with strong intuition for reinforcement learning. - Proficiency across a modern stack, Node.js and Python on the backend and React/TypeScript on the frontend, with strong Kubernetes and Docker skills. - Comfort operating in a fast-paced startup environment with high ownership and long hours. - A track record of meaningful tenure and impact at previous companies. - Reinforcement-learning experience or an RL research background is a strong plus, though not required. - Bachelor's degree in computer science or a related technical field, or equivalent practical experience.