Full-Stack Software Engineer, Reinforcement Learning

ABOUT THE ROLE Build product interfaces, backend systems, and internal tools for a reinforcement learning data platform. You will own experiences that help research teams and external partners create, evaluate, and improve training data, working closely with research, operations, and go-to-market teams. WHAT YOU'LL DO - Develop tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior. - Build partner workflows for creating, submitting, testing, and iterating on 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 assurance, and training infrastructure. - Work with cross-functional teams to turn ambiguous needs into polished products and ship improvements quickly. WHAT WE'RE LOOKING FOR - At least 3 years of full-stack engineering experience building and shipping production systems, with technical field study or equivalent technical fundamentals. - 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, and experience owning products from design through deployment. - Experience building backend services, APIs, and databases, as well as dashboards, monitoring tools, or observability interfaces. - Comfort with cloud infrastructure, Docker, CI/CD, and production debugging. - Experience with data inspection, review, quality assessment, data collection, labeling, or evaluation tools is valuable. AWS, Kubernetes, Terraform, and Grafana experience is also useful. - Strong product judgment, independent execution, and communication across technical and non-technical teams. COMPENSATION & BENEFITS Base salary is $130,000 to $225,000 USD annually. Visa sponsorship and relocation support are available for qualified full-time candidates moving to the United States or Singapore. LOCATION On-site in San Francisco, California. Singapore-based and remote candidates with substantial overlap with San Francisco or Singapore time zones may also be considered.