Senior Full-Stack Software Engineer, Reinforcement Learning

Department: Engineering

Location: San Francisco

Employment Type: FullTime

About the Role

Join an AI infrastructure team building the product interfaces, backend services, and internal tools that support reinforcement learning data creation and evaluation. You will work with research engineers and external partners to turn complex needs into practical, polished software.

What You'll Do

• Build tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior.

• Create partner workflows for developing, submitting, testing, and improving environments and training data.

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

• Design backend services and APIs connecting task authoring, data collection, evaluation, quality review, and training infrastructure.

• Collaborate with research, operations, and go-to-market teams to ship and improve systems as needs evolve.

What We're Looking For

• At least 3 years of experience building and shipping production software, with end-to-end ownership of user-facing or internal products.

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

• Experience building backend services, APIs, and databases, plus hands-on cloud infrastructure, Docker, CI/CD, and production debugging.

• Experience creating dashboards, monitoring or observability interfaces, and tools for data inspection, review, or quality assessment.

• Background in data collection, labeling, annotation, evaluation platforms, developer tools, or partner-facing workflows is valuable.

Compensation & Benefits

Annual salary range: $130,000 to $225,000 USD. Visa sponsorship and relocation support are available for eligible full-time candidates moving to the United States or Singapore.

Location

Primarily on-site in San Francisco, California, with an office also available in Singapore. Remote candidates may be considered if they can maintain substantial time zone overlap with either location.