Machine Learning Systems Engineer

Anthropic, a pioneering AI research company, is seeking a Machine Learning Systems Engineer to join their Encodings and Tokenization team. This role represents a unique opportunity to work at the intersection of AI development and system optimization, directly impacting how AI models learn and process information.

The position requires an experienced engineer with 8+ years of software engineering experience and strong machine learning expertise. You'll be working in a hybrid environment from their San Francisco office, with a competitive salary range of $300,000 to $405,000 USD. The role combines technical depth with cross-functional collaboration, focusing on developing and optimizing tokenization systems crucial to Anthropic's AI training workflows.

As a Machine Learning Systems Engineer, you'll be instrumental in building the infrastructure that enables Anthropic's research progress. Your responsibilities will span from designing tokenization systems to optimizing encoding techniques, all while ensuring the company's commitment to reliable and interpretable AI systems. The role requires proficiency in Python and modern ML development practices, with opportunities to work on cutting-edge problems in AI development.

Anthropic offers an impressive benefits package, including competitive compensation, equity donation matching, generous vacation and parental leave, and flexible working hours. The company's commitment to developing AI responsibly and their approach to AI research as an empirical science sets them apart in the field. Working at Anthropic means joining a collaborative team focused on high-impact research that could shape the future of AI technology.

The ideal candidate will thrive in a research environment where engineering directly enables scientific progress. You'll have the opportunity to work with state-of-the-art AI systems while contributing to the company's mission of creating safe and beneficial AI. The role offers significant growth potential and the chance to work on challenging problems at the forefront of AI development.