Data Operations Manager

About the Role

As Data Operations Manager, you'll build and scale data operations across research teams working on frontier AI capabilities. You'll partner with researchers to design and execute data strategies, manage vendor relationships, and own the entire data pipeline from requirements to production. This role requires operational excellence combined with technical depth to understand what makes high-quality training data, but your focus will be on strategy and execution.

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

You may be a good fit if you:

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

Strong candidates may also 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