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. 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. This role focuses on iterating on data collection strategies to distill knowledge from human experts into our models, closing the loop in hours and days rather than months. You will collaborate closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well‑scoped, well‑designed products.
Full-Stack Software Engineer, Reinforcement Learning at Anthropic
On-site - San Francisco, CA
More jobs at AnthropicRequirements
Skills
- 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
- Built data collection, labeling, or annotation platforms — ideally ones that had to scale across many vendors or many task types
- Background building multi-tenant platforms with role-based access, audit trails, and vendor management workflows
- Experience with cloud infrastructure (GCP or AWS), Docker, and CI/CD pipelines
- Familiarity with LLM training, fine-tuning, or evaluation workflows
- Experience with async Python (Trio, asyncio) or high-throughput API design
- Background in dashboards, monitoring, or observability tooling
- Experience working directly with external vendors or partners on technical integrations
- A background that isn't a straight line — e.g. math or physics into SWE, competitive programming, research into engineering, or a side project that outgrew its scope
Responsibilities
- 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
- Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking
- 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
- Develop 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
Technologies
PythonReactTypeScriptDockerCI/CDGCPAWSasync PythonTrioasyncioLLM trainingfine-tuningevaluation workflows
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