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.