Full-Stack Software Engineer, Reinforcement Learning

About the Role

You'll be a full-stack engineer building the product surfaces, backend systems, and internal tools that power an AI-focused reinforcement learning data engine. Sitting at the intersection of engineering, research, and operations, you'll own end-to-end product experiences that help frontier AI labs and external partners create, evaluate, and iterate on RL training data.

What You'll Do

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

Develop vendor-facing workflows that make it easy for external partners to create, submit, test, and iterate on RL environments and training data.

Create dashboards and observability tools surfacing environment quality, eval results, data collection progress, and pipeline health.

Design backend services and APIs connecting task authoring, data collection, evaluation, QA/QC, and RL training infrastructure.

Partner with research, operations, and go-to-market teams to ship well-designed systems quickly without waiting for perfect specs.

What We're Looking For

3+ years of full-stack software engineering experience building and shipping production systems.

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 tools for data inspection, review workflows, or quality assessment interfaces.

Experience building backend services, APIs, and databases that connect multiple system components.

Hands-on experience with cloud infrastructure, Docker, CI/CD pipelines, and production debugging.

Experience building dashboards, monitoring tools, or observability interfaces for complex systems.

Good product taste with the ability to build intuitive tools for both technical and non-technical users.

High agency: you identify what needs to exist, build it, and improve it independently.

Strong communication skills across research, engineering, and operations stakeholders.

Familiarity with AWS, Kubernetes, Terraform, or Grafana is a plus.

Background in data collection, labeling, annotation, or evaluation platforms is a plus.

Compensation & Benefits

Competitive compensation with full medical, dental, and vision coverage (US employees), 401k, commuter benefits, PTO plus a company-wide holiday break, and an Equinox membership. Visa sponsorship and relocation support are available for strong candidates moving to either the US or Singapore.

Location

Offices in San Francisco, CA and Singapore. Remote candidates are welcome provided they can maintain 70 to 80% time zone overlap with one of those locations.