About the role
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.