About the Role
Build product interfaces, backend systems, and internal tools for a reinforcement learning data platform. You will own experiences that help research teams and external partners create, evaluate, and improve training data, working closely with research, operations, and go-to-market teams.
What You'll Do
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Develop tools for browsing environments, inspecting trajectories, reviewing task quality, and understanding model behavior.
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Build partner workflows for creating, submitting, testing, and iterating on reinforcement learning environments and training data.
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Create dashboards and observability tools for environment quality, evaluation results, data collection progress, and pipeline health.
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Design backend services and APIs that connect task authoring, data collection, evaluation, quality assurance, and training infrastructure.
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Work with cross-functional teams to turn ambiguous needs into polished products and ship improvements quickly.
What We're Looking For
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At least 3 years of full-stack engineering experience building and shipping production systems, with technical field study or equivalent technical fundamentals.
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Proficiency in Python and a modern web stack such as React, TypeScript, or Next.js.
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Hands-on experience with AI, machine learning, or reinforcement learning, and experience owning products from design through deployment.
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Experience building backend services, APIs, and databases, as well as dashboards, monitoring tools, or observability interfaces.
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Comfort with cloud infrastructure, Docker, CI/CD, and production debugging.
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Experience with data inspection, review, quality assessment, data collection, labeling, or evaluation tools is valuable. AWS, Kubernetes, Terraform, and Grafana experience is also useful.
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Strong product judgment, independent execution, and communication across technical and non-technical teams.
Compensation & Benefits
Base salary is $130,000 to $225,000 USD annually. Visa sponsorship and relocation support are available for qualified full-time candidates moving to the United States or Singapore.
Location
On-site in San Francisco, California. Singapore-based and remote candidates with substantial overlap with San Francisco or Singapore time zones may also be considered.
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