Director, Product Management - Agents and Intelligence
Listed on 2026-10-10
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IT/Tech
AI Engineer (Applied/Software), AI Business & Operations
AI is creating effectively unlimited demand for insight while the traditional ways of producing it remain constrained. Meta cannot meet that demand by asking people to write every query, investigate every fast mover, or manually assemble every analysis. We need intelligent systems that can do the routine work at scale while enabling people to focus their creativity on the questions only they can ask.
The Data Analytics Tooling & Agents (DATA) organization is building that future. We are looking for a senior product lead to own the Intelligence & Execution layer: the agentic capabilities that understand intent, reason over Meta’s data and context, plan and execute analytical work, learn from feedback, and take safe action.
Your goal is to make these capabilities durable across models and interfaces, and to make AI the default way people across Meta turn questions into decisions. Success means materially improving the quality, complexity, and impact of the work our agents can complete quarter over quarter.
- 12+ years of experience in Product Management and/or equivalent relevant experience
- 12+ years of experience working collaboratively with engineering, design and user research teams
- 8+ years of experience hiring, managing, and developing both individual contributors and senior leaders
- Critical thinking/analytical leadership experience
- Experience presenting to senior executive audiences
- BA/BS in Computer Science or related field
- Experience working with Agentic products and workflows (e.g. evals, semantic layer, skills)
- Experience working on Analytics/enterprise cloud data platforms
- Experience building 0-1 products, platform/ecosystem products, or marketplaces
- 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
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