Machine Learning Systems Engineer, Research Tools

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, Research Tools 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)

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

Key skills/competency

• Machine Learning Systems
• Data Tokenization
• Encoding Techniques
• ML Infrastructure
• Python Programming
• Research Collaboration
• Performance Optimization
• Distributed Systems
• Debugging
• Testing Frameworks