Research Engineer, Code RL (Reinforcement Learning)

About Anthropic

Anthropics 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.

About the RL Teams

Our Reinforcement Learning teams play a critical role in advancing our AI systems. Weve 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 Anthropics 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.

About the Role

Were hiring for the Code RL team within the RL organization. As a Research Engineer youll 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. Youll 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 doesnt) 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 well match you to the area where youll have the most impact.

You may be a good fit if you:

• 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

Strong candidates may also have:

• 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

Related roles

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

Logistics

Minimum education: Bachelors 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 arent 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 youre interested in this work. We think AI systems like the ones were 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 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 youre ever unsure about a communication dont click any linksvisit for confirmed position openings.

How were 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. Were 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 aboutour policy for using AI in our application process.