Product Data Operations Program Manager

Meta is seeking a Program Manager to lead AI solutions and product data operations programs that support our AI Monetization & Foundational products. Meta is seeking a Product Operations Manager to support the Central and Standalone Products organization. In this role, you will oversee end-to-end data operations programs spanning AI model training pipelines, data quality initiatives, and annotation workflows - ensuring that product teams have the high-quality data they need to build and scale AI-driven features. You will collaborate closely with researchers, engineers, product, and operations partners to define program strategies, resolve cross-functional dependencies, and translate complex data challenges into actionable, scalable solutions.

Responsibilities

Manage and deliver product data operations programs that support AI model training, evaluation, and deployment pipelines across multiple product teams Partner with research, engineering, and product teams to define data requirements, align on quality standards, and prioritize data collection and annotation efforts for AI solutions Identify and resolve bottlenecks in data labeling, annotation, and curation workflows to ensure timely delivery of high-quality training datasets Break down complex data operations challenges into manageable components, applying systematic analysis to design and implement scalable solutions aligned with AI product roadmaps Develop and maintain program documentation including data governance frameworks, workflow specifications, risk registers, and milestone tracking for AI data initiatives Engage team leaders and cross-functional stakeholders to build alignment on program direction, surface risks proactively, and drive decisions that unblock data operations work Track and communicate program health metrics - including data throughput, quality rates, and delivery timelines - adapting communication style and format to technical and non-technical audiences Leverage AI tools and workflow automation to improve the efficiency and quality of data operations processes, sharing learnings to scale adoption across the team Contribute to team-level goal setting 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

Currently has, or is in the process of obtaining a Bachelor's degree in a directly related field, or equivalent practical experience. Degree must be completed prior to joining Meta 6+ years of experience in program management, data operations, or technical operations roles supporting AI, machine learning, or data-driven product development; or 2+ years of such experience with a Bachelor's degree in Computer Science, Artificial Intelligence, Computer Engineering, Human-Computer Interaction, or a related technical field Fundamental understanding of AI/ML model development and the data requirements across the model lifecycle - including training, evaluation, and fine-tuning data needs Experience managing cross-functional programs involving data pipelines, data labeling, annotation workflows, or AI training data quality initiatives Experience in analyzing operational data and communicating findings and recommendations to technical and non-technical stakeholders at varying levels of leadership Experience identifying process inefficiencies and implementing scalable solutions within data or AI operations environments Master's degree in Computer Science, Artificial Intelligence, Computer Engineering, Human-Computer Interaction, or a related technical field Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience working directly with research or machine learning teams to define data requirements and evaluate the quality of datasets for AI model development Experience managing vendor or outsourced data annotation and labeling operations at scale Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Familiarity with data governance practices, metadata management, or compliance considerations relevant to AI training data Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience using AI-powered tools or workflow automation platforms to redesign and accelerate data operations processes, including demonstrated application of responsible and ethical AI practices such as bias mitigation and quality review