Program Manager, Product Data Operations

Meta•Menlo Park, CA

23h•$132,000 - $189,000

About The Position

Meta is seeking a Program Manager to lead AI solutions and product data operations programs that directly support Meta Superintelligence Lab. 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.

Requirements

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

Nice To Haves

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
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, including demonstrated application of responsible and ethical AI practices such as bias mitigation and quality review
Experience managing vendor or outsourced data annotation and labeling operations at scale
Master's degree in Computer Science, Artificial Intelligence, Computer Engineering, Human-Computer Interaction, or a related technical field
Experience working directly with research or machine learning teams to define data requirements and evaluate the quality of datasets for AI model development
Familiarity with data governance practices, metadata management, or compliance considerations relevant to AI training data

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