Machine Learning Systems Engineer, Research Tools

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.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$320,000

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$405,000 USD

Logistics

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.

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

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.

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