About the job
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
Qualifications
Minimum
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
Preferred
Working with machine learning data processing pipelines
Building or optimizing data encodings for ML applications
Implementing or working with BPE, WordPiece, or other tokenization algorithms
Performance optimization of ML data processing systems
Multi-language tokenization challenges and solutions
Research environments where engineering directly enables scientific progress
Distributed systems and parallel computing for ML workflows
Large language models or other transformer-based architectures (not required)