Full-Stack Software Engineer, Reinforcement Learning

About the Role

Build the product interfaces, backend systems, and internal tools that support an AI reinforcement learning data platform. You will work with research engineers and external partners to turn complex requirements into intuitive products for creating, evaluating, and improving training data.

Compensation & Benefits

Base salary is $130,000 to $225,000 USD annually. Visa sponsorship is available for eligible full-time candidates.

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.

Responsibilities

Develop tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior.; Build partner workflows for creating, submitting, testing, and iterating on 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.; Work across research, operations, and go-to-market teams to ship and improve useful systems.

Requirements

At least 3 years of full-stack software engineering experience building and shipping production systems, with a technical education or background.; 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 a track record of owning products from design through deployment.; Experience building backend services, APIs, and databases, as well as using cloud infrastructure, Docker, CI/CD, and production debugging practices.; Experience with dashboards, monitoring or observability tools, data inspection, review workflows, or quality assessment interfaces.; Experience with data collection, annotation, or evaluation platforms, and tools such as AWS, Kubernetes, Terraform, or Grafana, is valuable.; Strong product judgment, initiative, and communication across technical and non-technical teams.