Machine Learning Systems Engineer, Reinforcement Learning Engineering
: Job Details :

About the role:

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 RL Eng 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. You're energized by the challenge of supporting and empowering our research team in the mission to build beneficial AI systems.

Our finetuning researchers train our production Claude models and internal research models using RLHF and other 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.

You may be a good fit if you:

Have 2+ 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 (we love to pair!)

Want to learn more about machine learning research

Care about the societal impacts of your work

Strong candidates may also have experience with:

High performance, large scale distributed systems

Kubernetes

Python

Machine learning

Implementing LLM finetuning algorithms, such as RLHF

Representative projects:

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 finetuning 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

Deadline to apply: None. Applications will be reviewed on a rolling basis.

Company:

Anthropic

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