✦ Luna Orbit — AI & Machine Learning

Machine Learning Systems Engineer, Research Tools

at Anthropic

📍 San Francisco, CA | New York City, NY | Seattle, WA Hybrid Posted March 07, 2026
Type Not Specified
Experience mid
Exp. Years Not specified
Education Not specified
Category AI & Machine Learning

This role focuses on developing and optimizing tokenization and encoding systems for AI research, supporting efficient and reliable model training workflows.

  • Design tokenization systems
  • Optimize encoding techniques
  • Collaborate with research teams
  • Build data pipelines
  • Implement monitoring and debugging

The position involves software engineering, ML infrastructure, tokenization algorithms, data pipelines, and multi-language support, primarily using Python.

The ideal candidate is a mid-level machine learning systems engineer with experience in developing tokenization and encoding systems, proficient in Python, and capable of working independently in research-focused environments.

software engineering experiencemachine learning expertisePython proficiencyexperience with ML data pipelinesbuilding or optimizing data encodings
working with ML data processing pipelinestokenization algorithmsperformance optimizationmulti-language tokenizationresearch environment experience
PythonML pipelinesTokenization algorithmsData processing systems
Machine LearningResearch ToolsTokenizationData PipelinesML InfrastructureModel trainingEncoding techniquesBPEWordPieceData representationMonitoringDebuggingTesting frameworksMulti-language tokenizationPython
Machine LearningResearch ToolsTokenizationData PipelinesML InfrastructureModel trainingEncoding techniquesBPEWordPieceData representationMonitoringDebuggingTesting frameworksMulti-language tokenization
collaborationproblem-solvingindependent workanalytical skillsimpact-drivenflexibilitycommunication
Industry Technology / AI & Machine Learning / Research
Job Function ML research infrastructure and systems engineering
Machine Learning Systems EngineerResearch ToolsTokenizationData PipelinesML InfrastructureModel trainingEncoding techniquesBPEWordPieceData representationMonitoringDebuggingTesting frameworksMulti-language tokenizationPythonMachine Learning

Lack of machine learning expertise, No experience with ML data pipelines, Inability to work independently, No proficiency in Python

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