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
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 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)