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.
Our Reinforcement Learning teams play a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of our latest Claude models. Our work spans several key areas:
Developing systems that enable models to use computers effectively
Advancing code generation through reinforcement learning
Pioneering fundamental RL research for large language models
Building scalable RL infrastructure and training methodologies
Enhancing model reasoning capabilities
We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.
We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely.
This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact.
Have strong software-engineering skills and deep Python expertise, including async/concurrent programming
Are comfortable owning systems end to end and debugging across the stack
Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results
Care about code quality, testing, and performance
Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Experience with reinforcement learning, RLHF, post-training, or LLM finetuning
Built coding agents, code-execution sandboxes, eval harnesses, verifiers, or developer tooling
Background in program analysis, testing, verification, compilers, or formal methods
Experience with PyTorch and large-scale distributed training; performance profiling and optimization of ML systems
CUDA / GPU or TPU kernel experience and accelerator-performance intuition
Experience with virtualization and sandboxed code execution environments
If your background leans toward one of these areas specifically, you may also want to look at these postings:
Research Engineer, Performance RL (Reinforcement Learning) — teaching Claude to write correct, fast code for accelerators
Research Engineer, Universes — long-horizon, ultra-realistic agentic training environments
Research Engineer, Cybersecurity RL (Reinforcement Learning) — RL for security-relevant coding capabilities
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.
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.
For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
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.
Develop and implement reinforcement learning approaches for defensive cybersecurity, designing environments, running experiments and evaluations, and delivering work into production training runs for safer AI models.
Develop and evaluate reinforcement-learning environments and agentic systems that advance AI capabilities in defensive cybersecurity, translating research into production training runs and collaborating across research and security teams.
Improve the judgment and reliability of production AI security agents by advancing their reasoning, evaluations, model selection, and defenses against attacks such as prompt injection.
Build and operate security controls for large-scale reinforcement learning systems, reviewing code and designs, developing shared tooling and observability, and balancing security with training correctness and performance.
Build and own infrastructure for post-training open-weight models, including distributed training and rollouts, agent environments, evaluation tools, and reliable production integration in close partnership with researchers.
