Full-Stack Software Engineer, Reinforcement Learning

Job Description

Job Description

About the Role

Build the product interfaces, backend services, and internal tools that support an AI reinforcement learning data platform. You will work with research, operations, and external partners to turn complex needs into useful tools for creating, evaluating, and improving training data.

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 review, and training systems.

• Collaborate across research, operations, and go-to-market teams to ship and improve products.

What We're Looking For

• At least 3 years of full-stack software engineering experience building and shipping production systems, with a technical field of study or equivalent technical foundation.

• 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 end-to-end from design through deployment.

• Experience building backend services, APIs, and databases, along with cloud infrastructure, Docker, CI/CD, and production debugging.

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

• Experience with data collection, annotation, or evaluation platforms, or with AWS, Kubernetes, Terraform, or Grafana, is valuable.

Compensation & Benefits

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

Location

On-site in San Francisco, California. Remote candidates may be considered if they can maintain 70% to 80% time zone overlap with San Francisco or Singapore.