Full Stack Software Engineer, Reinforcement Learning

About Anthropic

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.

About the Role

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.

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 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

You May Be a Good Fit If You:

• 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

Strong Candidates May Also Have:

• 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

Representative Projects:

• Building a unified platform for human data collection that integrates labeling workflows, vendor management, and quality assurance 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 provide real-time feedback during production training

• Building environment quality review workflows that allow researchers to browse, grade, and provide feedback on training environments

• Developing automated environment quality pipelines that validate correctness and difficulty calibration before deployment to production training

• Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

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.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process