Data Operations Manager, Human Data
San Francisco, CA | New York City, NY
Posted 16d ago
About the job
Anthropic is building reliable, interpretable, and steerable AI systems to be safe and beneficial for users and society. As the Data Operations Manager, you will be instrumental in building and scaling data operations for research teams focused on frontier AI capabilities. This role involves partnering with researchers to define and execute data strategies, managing vendor relationships, and overseeing the entire data pipeline from inception to production. While a strong understanding of what constitutes high-quality training data is important, the primary focus will be on strategic planning and execution to ensure the data operations directly contribute to model performance in critical areas like tool use accuracy, prompt injection robustness, and safety alignment.
Responsibilities
- Own and execute data strategy for research teams advancing frontier AI capabilities across RLHF, safety, tool use, and agentic workflows.
- Drive strategic vendor partnerships and build scalable frameworks for technical data collection at scale.
- Design and implement operational systems that translate research requirements into high-quality data pipelines.
- Build evaluation frameworks and quality standards that ensure data meets the bar for training state-of-the-art AI systems.
- Lead cross-functional initiatives to optimize research velocity while maintaining rigorous quality standards.
- Proactively identify risks, bottlenecks, and opportunities to improve efficiency and effectiveness across data operations.
- Partner with senior research leaders to align data operations with model development roadmaps and strategic priorities.
Requirements
- 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 ability to engage effectively with technical and non-technical stakeholders.
- Familiarity with how LLMs work or strong interest in understanding AI training methodologies.
- Highly organized and able to navigate ambiguity effectively.
- Experience with data analysis tools (SQL, Python, Tableau, spreadsheets, or similar).
- Ability to thrive in fast-paced research environments with shifting priorities.
- Passion for AI safety and understanding the critical importance of high-quality data.