Meta is seeking a Product Data Operations Program Manager to drive AI solutions and data programs that power intelligent products across Meta's portfolio. In this role, you will manage end-to-end data operations programs that support AI model development, training data pipelines, and data quality initiatives - enabling teams to build and ship AI-driven features at scale. You will partner with data science, engineering, product, and operations teams to define program strategies, resolve data pipeline dependencies, and ensure high-quality data outputs that directly influence AI product outcomes.
Responsibilities
Manage and deliver data operations programs that support AI model training, evaluation, and deployment pipelines across product teams
• Partner with data science, engineering, and product teams to define data requirements, prioritize data collection efforts, and align on quality standards for AI solutions
• Identify and resolve bottlenecks in data labeling, annotation, and curation workflows to ensure timely delivery of high-quality training datasets
• Analyze complex data operations challenges and propose scalable solutions that align with AI product roadmaps and organizational goals
• Develop and maintain program documentation, including data governance frameworks, workflow specifications, and milestone tracking for AI data initiatives
• Engage team leaders and cross-functional stakeholders to build alignment on program direction, surface risks early, and drive decisions that unblock data operations work
• Leverage AI tools and workflow automation to improve the efficiency and quality of data operations processes, sharing learnings to scale adoption across the team
• Track and communicate program health metrics - including data throughput, quality rates, and delivery timelines - to stakeholders at varying leadership levels
• Provide input into team-level goals by synthesizing insights from data operations performance and translating them into actionable recommendations for AI product teams
• Adapt program plans in response to shifting AI product priorities, regulatory requirements, or data availability constraints, maintaining focus on highest-impact deliverables
Minimum Qualifications
• 2+ years of experience in program management, data operations, or technical operations roles supporting AI, machine learning, or data-driven product development
• Experience managing cross-functional programs involving data pipelines, data labeling, annotation workflows, or AI training data quality initiatives
• Experience analyzing operational data and communicating findings and recommendations to technical and non-technical stakeholders
• Experience building and maintaining program tracking systems, documentation, and reporting frameworks for complex, multi-team initiatives
• Experience identifying process inefficiencies and implementing scalable solutions within data or AI operations environments
Preferred Qualifications
• Familiarity with data governance practices, metadata management, or compliance considerations relevant to AI training data
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience working directly with data science or machine learning teams to define data requirements and evaluate dataset quality for AI model development
• Experience managing vendor or outsourced data annotation and labeling operations at scale
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience using AI-powered tools or workflow automation platforms to redesign and accelerate data operations processes
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
