Machine Learning Systems Engineer, RL Engineering

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.

You want to build the cutting‑edge systems that train AI models like Claude. You’re excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable, and steerable AI. As an ML Systems Engineer on our Reinforcement Learning Engineering team, you’ll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models.

Your work will directly enable breakthroughs in AI capabilities and safety. You’ll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible.

Our fine tuning researchers train our production Claude models and internal research models using RLHF and related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models. You’ll be responsible for improving the speed, reliability, and ease‑of‑use of these systems.

• Have 4+ years of software engineering experience
• Like working on systems and tools that make other people more productive
• Are results‑oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming
• Want to learn more about machine learning research
• Care about the societal impacts of your work

• Python
• Implementing LLM fine tuning algorithms, such as RLHF

• Profiling our reinforcement learning pipeline to find opportunities for improvement
• Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline
• Making changes to our fine tuning systems so they work on new model architectures
• Building instrumentation to detect and eliminate Python GIL contention in our training code
• Diagnosing why training runs have started slowing down after some number of steps, and fixing it
• Implementing a stable, fast version of a new training algorithm proposed by a researcher

Compensation: $500,000 – $850,000 USD per year.

Benefits:
Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a collaborative office environment.

We do not discriminate on the basis of any protected group status under any applicable law.