Full-Stack Software Engineer, Reinforcement Learning
Anthropic · Added 3 days ago
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AI Safety & Alignment
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.
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.
This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn't typing — it's judgment, taste, and the ability to react to what researchers need next. You'll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you'll do it in a loop that closes in hours and days, not quarters or months.
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 impact on the autonomy and coding capabilities of our most advanced models. Our work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models.
The RL org is organized around four 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. 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.
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
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
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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$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.
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.
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.
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.
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