Research Engineer, Code RL (Reinforcement Learning)

Anthropic; New York (NY); Hybrid; USD 500.000 - 850.000; Vollzeit

Zusammenfassung

Anthropic Limited in New York is hiring a Research Engineer for the Code RL team. You will help our models write, edit, test, debug, and ship real software end-to-end, working on real codebases with real tools.

This role blends research and engineering: design RL environments, build reward signals, run experiments on frontier models, and improve pipelines for faster, safer software development.

Qualifikationen

• Bachelor's degree or equivalent required.
• Field of study relevant to the role as demonstrated through coursework or experience.
• Location-based hybrid policy requiring 25% office presence.

Aufgaben

• Design RL environments and coding tasks.
• Build reward signals and verifiers for code quality.

Kenntnisse

Software engineering

Python

Async programming

End-to-end ownership

Debugging across stack

Code quality

AI safety focus

Ausbildung

Bachelor's degree

Tools

PyTorch

Distributed training

CUDA

TPU

Jobbeschreibung

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.

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.

About The RL Teams

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.

About The Role

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.

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

• 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

Annual Salary

$500,000—$850,000 USD

Logistics

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.

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.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process.