Machine Learning Systems Engineer, Encodings and Tokenization

Anthropic is seeking an experienced Machine Learning Systems Engineer to join their Encodings and Tokenization team. This role represents a unique opportunity to work at the intersection of machine learning infrastructure and research, focusing on developing critical systems that power AI model training.

The position involves building and optimizing tokenization systems that are fundamental to Anthropic's AI research progress. As a bridge between Pretraining and Finetuning teams, you'll be responsible for creating infrastructure that enables more efficient and effective training of AI systems while maintaining their reliability and interpretability.

The ideal candidate brings 8+ years of software engineering experience with strong machine learning expertise. You'll work in a collaborative environment that values pair programming and emphasizes the societal impact of AI development. The role requires proficiency in Python, experience with ML infrastructure, and the ability to work effectively in a research-driven environment.

Anthropic offers a competitive compensation package ranging from $300,000 to $405,000 USD annually, along with comprehensive benefits including equity donation matching, generous leave policies, and flexible working arrangements. The position follows a hybrid work model requiring at least 25% in-office presence in either San Francisco or New York City.

The company stands out for its approach to AI research, treating it as an empirical science similar to physics and biology. They focus on large-scale research efforts aimed at developing steerable, trustworthy AI systems. Their work builds on significant research achievements including GPT-3, Circuit-Based Interpretability, and Learning from Human Preferences.

This role offers the opportunity to work on cutting-edge AI technology while contributing to Anthropic's mission of ensuring AI systems are safe and beneficial for society. The position combines technical challenges with meaningful impact, making it ideal for engineers who care about both technical excellence and ethical AI development.