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.