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.

• 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.