Full-Stack Software Engineer, Reinforcement Learning (Mid-Level)

About this role

ABOUT THE ROLE

Build product interfaces, backend services, and internal tools for an AI infrastructure platform focused on reinforcement learning data and evaluation. You will work closely with research, operations, and external partners to turn complex needs into useful, reliable products.

WHAT YOU'LL DO

- Develop interfaces for browsing environments, inspecting model trajectories, reviewing task quality, and understanding model behavior.

- Build workflows that help external partners create, submit, test, and improve reinforcement learning environments and training data.

- Create dashboards and observability tools for environment quality, evaluation results, data collection progress, and pipeline health.

- Design backend services and APIs that connect task authoring, data collection, evaluation, quality review, and training systems.

- Collaborate with research, operations, and go-to-market teams to ship and improve products in a fast-moving environment.

WHAT WE'RE LOOKING FOR

- At least 3 years of experience building and shipping production full-stack software.

- Proficiency in Python and a modern web stack such as React, TypeScript, or Next.js.

- Hands-on experience with AI, machine learning, or reinforcement learning, plus end-to-end ownership of user-facing or internal products.

- Experience building data inspection, review, quality assessment, dashboard, or observability tools.

- Experience with backend services, APIs, databases, cloud infrastructure, Docker, CI/CD, and production debugging.

- Clear communication skills and the ability to work across technical and non-technical teams.

- Experience with AWS, Kubernetes, Terraform, Grafana, developer tools, partner workflows, or data collection and evaluation platforms is a plus.

COMPENSATION & BENEFITS

Salary range is $130,000 to $225,000 USD annually. Benefits include health coverage, paid time off, and retirement benefits. Visa sponsorship is available for qualified full-time candidates.

LOCATION

On-site in San Francisco, California, United States.