Full-Stack Software Engineer, Reinforcement Learning

• Develop tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior.
• Build partner workflows for creating, submitting, testing, and improving 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 infrastructure.
• Collaborate with research, operations, and go-to-market teams to ship and improve systems.
• At least 3 years of full-stack software engineering experience building and shipping production systems.
• Hands-on experience with AI, machine learning, or reinforcement learning.
• Proficiency in Python and a modern web stack such as React, TypeScript, or Next.js.
• Experience owning user-facing or internal products end-to-end, from design through deployment.
• Experience building backend services, APIs, and databases, plus practical knowledge of cloud infrastructure, Docker, CI/CD, and production debugging.
• Background building dashboards, monitoring or observability interfaces, data inspection tools, review workflows, or quality assessment products.
• Experience with data collection, labeling, annotation, or evaluation platforms is valuable. AWS, Kubernetes, Terraform, Grafana, developer tools, and partner-facing workflows are also relevant.
• On-site in San Francisco, California. Candidates able to maintain substantial time zone overlap with San Francisco or Singapore may also be considered for remote work.

None flagged.