Full-Stack Software Engineer, Reinforcement Learning
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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 that surface 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 they'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 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.
- Experience building tools for data inspection, review workflows, or quality assessment interfaces.
- High agency: ability to translate ambiguous requirements into shipped features without detailed specifications.
- Strong communication skills across technical and non-technical stakeholders.
Nice to have
- Experience with data collection, labeling, annotation, or evaluation platforms is a plus.
- Familiarity with AWS, Kubernetes, Terraform, or Grafana in a product-shipping context is a plus.
- Background building vendor-facing or partner-facing workflow products is a plus.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About the Role
This role sits at the intersection of product engineering and AI infrastructure, building the tools and systems that power a reinforcement learning data platform used by frontier AI labs and external partners. You will own end-to-end product experiences, from frontend dashboards to backend services, enabling partners to create, evaluate, and iterate on RL training data. Your work directly shapes how researchers and operators understand model behavior and environment quality.
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 that surface 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 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.
Experience building tools for data inspection, review workflows, or quality assessment interfaces.
High agency: ability to translate ambiguous requirements into shipped features without detailed specifications.
Strong communication skills across technical and non-technical stakeholders.
Experience with data collection, labeling, annotation, or evaluation platforms is a plus.
Familiarity with AWS, Kubernetes, Terraform, or Grafana in a product-shipping context is a plus.
Background building vendor-facing or partner-facing workflow products is a plus.
Compensation & Benefits
Competitive compensation with full medical, dental, and vision coverage for US-based employees, 401k, commuter benefits, PTO plus a company-wide holiday break, gym membership, and access to leading AI development tools. Visa sponsorship and relocation support are available for strong full-time candidates moving to the US or Singapore.
Location
Primary office location is San Francisco, CA. A Singapore office is also available. Remote candidates are welcome if they can maintain 70 to 80 percent time zone overlap with San Francisco or Singapore.
Company
Company facts come from this company's own listings. We only show what the postings themselves carry.