Machine Learning Systems Engineer, Anthropic

• We require at least a Bachelor's degree in a related field or equivalent experience
• 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
• Have strong analytical skills and can evaluate the impact of engineering changes on research outcomes
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Care about the societal impacts of your work and are committed to developing AI responsibly

Desirable

• 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
• You'll 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

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