Machine Learning Systems Engineer, Research Tools

San Francisco, CA | New York City, NY | Seattle, WAOn-site 2mo ago

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 Role:

We are seeking an experienced Machine Learning Systems Engineer to join our Encodings and Tokenization team at Anthropic. This cross-functional role will be instrumental in developing and optimizing the encodings and tokenization systems used throughout our Finetuning workflows. As a bridge between our Pretraining and Finetuning teams, you'll build critical infrastructure that directly impacts how our models learn from and interpret data. Your work will be foundational to Anthropic's research progress, enabling more efficient and effective training of our AI systems while ensuring they remain reliable, interpretable, and steerable.

Responsibilities:

Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
Optimize encoding techniques to improve model training efficiency and performance
Collaborate closely with research teams to understand their evolving needs around data representation
Build infrastructure that enables researchers to experiment with novel tokenization approaches
Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
Create robust testing frameworks to validate tokenization systems across diverse languages and data types
Identify and address bottlenecks in data processing pipelines related to tokenization
Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams

You May Be a Good Fit If You:

Have significant software engineering experience with demonstrated machine learning expertise
Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
Can work independently while maintaining strong collaboration with cross-functional teams
Are results-oriented, with a bias towards flexibility and impact
Have experience with machine learning systems, data pipelines, or ML infrastructure
Are proficient in Python and familiar with modern ML development practices

Apply now

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