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    Machine Learning Systems Engineer, Finetuning Infrastructure - San Francisco, CA, United States - Anthropic Limited

    Anthropic Limited
    Anthropic Limited San Francisco, CA, United States

    2 weeks ago

    Default job background
    Description
    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 Engineer on our Finetuning Infrastructure 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.

    About the role
    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 significant software engineering experience
    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
    Like working on systems and tools that make other people more productive
    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.
    #J-18808-Ljbffr
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