Research Engineer, Code RL (Reinforcement Learning) San Francisco, CA | New York City, NY

Anthropic; San Francisco (CA); On-site; USD 500,000 - 850,000; Full time

Job summary

Anthropic in San Francisco is looking for a Research Engineer to enhance AI systems by advancing their coding abilities—encompassing writing, editing, testing, and debugging real software.

You will design RL tasks and develop reward signals, run training experiments, and play a pivotal role in shaping the future of beneficial AI systems.

The compensation range for this position is USD $500,000 – $850,000 annually.

Qualifications

• Strong software-engineering skills with deep Python expertise, including async/concurrent programming.
• Experience owning systems end-to-end and debugging across the stack.
• Balance research exploration with engineering implementation.

Responsibilities

• Advance models' ability to write, edit, test, debug, and ship real software.
• Design RL environments and coding tasks, build the reward signals.
• Run training experiments on frontier models.

Skills

Software-engineering skills

Deep Python expertise

Debugging across the stack

Knowledge of code quality

Experience with reinforcement learning

Tools

PyTorch

CUDA

TPU

Job description

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

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

We have posted other roles that may align with your background. Please review the attached postings. 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.

USD $500,000 – $850,000

Equal Employment Opportunity

As set forth in Anthropic’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.