Data Operations Manager

Anthropic Anthropic · AI Frontier · AI Research & Engineering

This role focuses on building and scaling data operations for AI research teams, managing the entire data pipeline from requirements to production. It involves partnering with researchers, managing vendors, and ensuring high-quality training data for frontier AI capabilities like RLHF, safety, tool use, and agentic workflows. The role requires operational excellence, technical depth in understanding training data, and strong project management skills.

What you'd actually do

  1. Own and execute data strategy for research teams advancing frontier AI capabilities across RLHF, safety, tool use, and agentic workflows
  2. Drive strategic vendor partnerships and build scalable frameworks for technical data collection at scale
  3. Design and implement operational systems that translate research requirements into high-quality data pipelines
  4. Build evaluation frameworks and quality standards that ensure data meets the bar for training state-of-the-art AI systems
  5. Lead cross-functional initiatives to optimize research velocity while maintaining rigorous quality standards

Skills

Required

  • 3+ years in operations, consulting, product management, or program management roles
  • Exceptional project management skills with ability to handle multiple complex projects simultaneously
  • Strong communication skills and can engage effectively with technical and non-technical stakeholders
  • Familiar with how LLMs work or have strong interest in understanding AI training methodologies
  • Highly organized and can navigate ambiguity effectively
  • Experience with data analysis tools (SQL, Python, Tableau, spreadsheets, or similar)
  • Thrive in fast-paced research environments with shifting priorities
  • Passionate about AI safety and understand the critical importance of high-quality data

Nice to have

  • Experience with data collection, labeling, or annotation operations for AI/ML systems
  • Knowledge of RLHF, constitutional AI, or human-in-the-loop workflows
  • Background working with research teams at AI companies or research-oriented organizations
  • Experience managing vendor relationships or external contractors
  • Consulting background with experience translating complex requirements into deliverables
  • Track record of implementing process improvements or quality control systems at scale

What the JD emphasized

  • high-quality training data
  • frontier AI capabilities
  • operational excellence
  • technical depth
  • scaling quality
  • nuanced technical requirements
  • AI safety

Other signals

  • data strategy
  • vendor partnerships
  • data pipeline
  • training data quality
  • research velocity
  • operational excellence
  • AI safety