Research Engineer, Code RL (Reinforcement Learning)

United States Digital Space LLC; San Francisco (CA); Hybrid; USD 500,000 - 850,000; Full time

Job summary

United States Digital Space LLC in San Francisco is hiring a Research Engineer for the Code RL team. This position aims to enhance AI models' software capabilities using deep Python expertise.

The role involves designing RL environments, managing training experiments, and ensuring code quality. Candidates are expected to possess strong software-engineering skills and a passion for AI. The position offers a hybrid work policy and an annual salary range of $500,000 to $850,000 USD.

Qualifications

• Strong software-engineering skills and deep Python expertise, including async/concurrent programming.
• Comfortable owning systems end to end and debugging across the stack.
• Able to engage rigorously in shaping experimental design and interpreting results.

Responsibilities

• Advance our models' ability to write, edit, test, debug, and ship real software.
• Design RL environments and coding tasks to capture 'good code'.
• Run training experiments on frontier models for improvements.

Skills

Strong software-engineering skills

Deep Python expertise

Comfortable debugging across the stack

Understanding of code quality and testing

Passion for AI

Education

Bachelor’s degree or equivalent

Tools

PyTorch

CUDA

Job description

About the company

the company’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 the company'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 – teaching Claude to write correct, fast code for accelerators
• Research Engineer, Universes – long‑horizon, ultra‑realistic agentic training environments
• Research Engineer, Cybersecurity RL – RL for security‑relevant coding capabilities

The annual compensation range for this role is listed below.

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.
• 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.

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