Product Data Operations Program Manager

Meta•Austin, TX

1d•$99,000 - $144,000

About The Position

Meta is seeking a Program Manager to lead AI solutions and product data operations programs supporting its AI Monetization & Foundational products and Central and Standalone Products organization. The role oversees end-to-end data operations programs spanning AI model training pipelines, data quality initiatives, and annotation workflows, ensuring product teams have the high-quality data needed to build and scale AI-driven features. The position works closely with researchers, engineers, product teams, and operations partners to define program strategies, resolve cross-functional dependencies, and turn complex data challenges into actionable, scalable solutions.

Requirements

Currently has or is pursuing a Bachelor's degree in a directly related field, or has equivalent practical experience; a degree must be completed before joining Meta.
At least 6 years of experience in program management, data operations, or technical operations supporting AI, machine learning, or data-driven product development; alternatively, at least 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 data requirements across the model lifecycle, including training, evaluation, and fine-tuning.
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 at varying levels of leadership.
Experience identifying process inefficiencies and implementing scalable solutions in data or AI operations environments.

Nice To Haves

Master's degree in Computer Science, Artificial Intelligence, Computer Engineering, Human-Computer Interaction, or a related technical field.
Ongoing AI skill development, such as prompt/context engineering or agent orchestration, and staying current with emerging AI technologies.
Experience working directly with research 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.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or 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, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Experience using AI-powered tools or workflow automation platforms to redesign and accelerate data operations, including applying responsible and ethical AI practices such as bias mitigation and quality review.

Responsibilities

Manage and deliver product data operations programs supporting 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 and 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, and drive decisions that unblock data operations work.
Track and communicate program health metrics, including data throughput, quality rates, and delivery timelines, for technical and non-technical audiences.
Use AI tools and workflow automation to improve the efficiency and quality of data operations processes, and share learnings to scale adoption across the team.
Contribute to team-level goal setting by synthesizing data operations performance insights into actionable recommendations for AI product teams.
Adapt program plans to changing AI product priorities, regulatory requirements, or data availability constraints while focusing on the highest-impact deliverables.