Machine Learning Systems Engineer, Research Tools

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

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

Strong Candidates May Also Have Experience With :

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)

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

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