Machine Learning Systems Engineer, Encodings and Tokenization at Anthropic

Candidates should possess significant software engineering experience with demonstrated machine learning expertise, proficiency in Python, and familiarity with modern ML development practices. A Bachelor's degree in a related field or equivalent experience is required. Experience with machine learning systems, data pipelines, or ML infrastructure is expected, and strong analytical skills are necessary to evaluate engineering changes. Experience with machine learning data processing pipelines, building or optimizing data encodings for ML applications, implementing tokenization algorithms like BPE or WordPiece, performance optimization of ML data processing systems, multi-language tokenization challenges, research environments, distributed systems, parallel computing for ML workflows, or large language models is a plus. Candidates should be comfortable navigating ambiguity, working independently while collaborating, and be results-oriented with a bias towards flexibility and impact. They should also care about the societal impacts of their work and be committed to developing AI responsibly.