Location: San Francisco, CA
Work Model: Onsite
Industry: Applied AI / AI research data
Compensation: $180K-$220K base, ~$400K+ OTE (uncapped profit share)
The OpportunityAs 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.
