AI Data Ops Program Manager — Data Quality & Workflows (Equity)

Meta; Austin (TX); Presencial; USD 132 000 - 189 000; Tempo integral

Resumo da oferta

Meta is seeking a Program Manager to lead AI data operations and product data workflows, spanning model training pipelines, data quality initiatives, and annotation processes. You will collaborate with researchers, engineers, product, and operations teams to define strategies, resolve cross-functional dependencies, and translate data challenges into scalable solutions.

The role focuses on end-to-end program delivery, governance, and driving improvements in data throughput, quality, and

Qualificações

• 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

Responsabilidades

• 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

Conhecimentos

Program management

Data operations

Cross-functional collaboration

AI/ML understanding

Data governance

Formação académica

Bachelor's degree in CS/AI/CE/HCI or related field

Master's degree in CS/AI/CE/HCI or related field

Ferramentas

AI tools

Workflow automation platforms

Data labeling tools

Descrição da oferta de emprego

Meta is seeking a Program Manager to lead AI data operations and product data workflows, spanning model training pipelines, data quality initiatives, and annotation processes. You will collaborate with researchers, engineers, product, and operations teams to define strategies, resolve cross-functional dependencies, and translate data challenges into scalable solutions.

The role focuses on end-to-end program delivery, governance, and driving improvements in data throughput, quality, and