Full Stack Software Engineer, Reinforcement Learning

Anthropic•San Francisco, CA

5h•Hybrid

About The Position

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic's Reinforcement Learning organization leads the research and development that trains Claude to be capable, reliable, and safe. We've contributed to every Claude model, with significant impacts on the autonomy and coding capabilities of our most advanced models. Our work spans developing systems that enable models to use computers effectively, advancing code generation through reinforcement learning, pioneering fundamental RL research for large language models, and building scalable training methodologies. The RL org is organized around four key goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model. We collaborate closely with Anthropic's alignment and safety teams to ensure our systems are both capable and safe. Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible: from realistic agentic training environments and scalable code data generation, to human data collection platforms and production training operations. As a Full-Stack Software Engineer within Reinforcement Learning, you'll build the platforms, tools, and interfaces that power RL environment creation, data collection, and training observability. Our ability to train frontier models depends on generating diverse, high-quality training data — and the products you build are what make that possible for researchers, vendors, and data labelers alike. This is a software engineering role embedded within research teams. You'll own product surfaces end-to-end — from backend services and APIs to web UIs that internal researchers, external vendors, and data labelers rely on daily. You don't need a background in ML research — what matters is strong full-stack engineering skills and the ability to build polished, reliable products in a fast-moving environment.

Requirements

Have strong software engineering fundamentals with full-stack experience
Are proficient in Python and modern web frameworks (React, TypeScript, or similar)
Have experience building and shipping user-facing products, internal tools, or developer platforms
Can own a product surface end-to-end — backend, frontend, API design, database schema
Have experience with relational databases, API design patterns, and authentication/authorization systems
Care about UX and can build interfaces that are intuitive for both technical and non-technical users
Communicate clearly with researchers, operations teams, and engineers and can translate ambiguous requirements into well-scoped work
Are motivated by building excellent platforms
Operate with high agency: you identify what needs to be done and drive it forward independently
Thrive in a fast-paced environment where priorities shift and new problems emerge regularly
Care about Anthropic's mission to build safe, beneficial AI and want your work to contribute to that goal

Nice To Haves

Experience building data collection, labeling, or annotation platforms
Background building multi-tenant platforms with role-based access 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 frameworks (Trio, asyncio) or high-throughput API design
Background building dashboards, monitoring, or observability tooling
Experience working with external vendors or partners on technical integrations

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 enable external partners to 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
Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward signal integrity
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, creating diverse programming tasks with robust reward signals across languages and difficulty levels
Develop onboarding automation and documentation tooling so new vendors and internal users can ramp up quickly
Collaborate with RL researchers, data operations, and vendor management teams to translate their needs into well-designed product experiences