Full-Stack Software Engineer, Reinforcement Learning

About the role

As a Full-Stack Software Engineer in RL, you'll build the platforms, tools, and interfaces that power environment creation, data collection, and training observability. The quality of Claude's next generation depends on the quality of the data we train it on — and the systems you build are what make that data possible.

You'll own product surfaces end-to-end — from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast.

What You'll Do

• Build and extend web platforms for RL environment creation, management, and quality review — including environment configuration, versioning, and validation workflows
• Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction
• Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early
• Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure
• Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels
• Create onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks
• Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products

Who We're Looking For

• Have strong software engineering fundamentals and real full-stack range — you're comfortable owning a surface from database schema to frontend
• Are proficient in Python and a modern web stack (React, TypeScript, or similar)
• Have a track record of shipping systems that solved a hard problem, not just shipped on time — e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible
• Operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket
• Have found yourself wondering "why isn't this moving faster?" in previous roles — and then have done something about it
• Care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers
• Communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work
• Thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before
• Care about Anthropic's mission to build safe, beneficial AI and want your work to contribute directly to it

Representative Projects

• Building a unified platform for human data collection that integrates labeling workflows, vendor management, and QA for complex agentic tasks
• Developing vendor onboarding automation that handles Docker registry access, API token management, and environment validation
• Creating evaluation and observability dashboards that catch reward hacks, measure environment difficulty, and give real-time feedback during production training
• Building environment quality review workflows that let researchers browse, grade, and provide feedback on training environments
• Developing automated environment quality pipelines that validate correctness and difficulty calibration before environments hit production training
• Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress

Compensation and Logistics

The annual compensation range for this role is $300,000 - $405,000 USD.

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.