Product Manager (Leadership) - Central Security

Meta•Menlo Park, CA

9d•$249,000 - $323,000

About The Position

Meta's Central Security organization protects people, products, infrastructure, and data while enabling the company to build and ship quickly. This leadership role supports cyber and information security at Meta and partners closely with Meta Superintelligence Labs (MSL) to secure AI products across research, product, and infrastructure. The role helps ensure these products meet Meta's standards for security, safety, and responsible deployment from the ground up. The position sets product vision and strategy across multiple large product areas or portfolios, shapes transformative long-term opportunities, and builds alignment across organizations and senior leadership. It leads product development and complex execution with engineering, design, legal, and other cross-functional partners; establishes AI-native strategies, evaluations, and data practices; and uses research, metrics, experimentation, and technical judgment to guide product decisions. The role also scales AI practices and workflows across the organization and maintains team health while driving products aligned with Meta's mission and competitive position.

Requirements

12+ years of experience in Product Management.
12+ years of experience working collaboratively with engineering, design, and user research teams.
5+ years of experience hiring, managing, and developing individual contributors and senior leaders.
Critical thinking and analytical leadership experience.
Demonstrated proficiency using AI-enabled tools to build product artifacts at scale.
Experience developing and championing AI-native strategies across organizations.
Experience presenting to senior executive audiences.
BA/BS in Computer Science or a related field, or equivalent practical experience.

Nice To Haves

Track record of scaling AI best practices across organizations.
Experience in a consumer-focused technology company.
Experience building 0-to-1 AI-native products, platform or ecosystem products, or marketplaces.
Background in Security Engineering and/or Software Engineering.

Responsibilities

Identify and shape transformative, long-term opportunities across multiple product areas or portfolios, setting vision and strategy that influence organizational and industry direction.
Build buy-in and consensus across organizations and senior leadership, bringing clarity to ambiguous opportunities and executing with minimal oversight.
Evaluate whether AI is the appropriate solution at portfolio level and set standards for rigorous tradeoff analysis.
Translate AI capabilities into differentiated product visions and champion AI-native strategies, evaluations, and data strategies.
Drive product development with engineering and design teams while maintaining team health.
Define product missions and requirements, coordinate cross-functional resources, develop roadmaps, and guide teams through milestones.
Use AI tools responsibly to reimagine workflows and improve organizational velocity and capability.
Promote rapid experimentation and learning practices; scale responsible AI practices, workflows, and artifacts across the organization.
Plan, initiate, and manage IT projects for web-based products, applications, and platforms.
Orchestrate complex execution across organizations using AI automation and strategic human oversight.
Use AI-enabled tools to build products and set a standard for PM technical capability.
Interpret research and state-of-the-art learnings to shape product strategy and apply rigorous reasoning.
Make strategic technical decisions with engineering leadership, accounting for system and architecture trade-offs and their effects on user experience and business outcomes.
Integrate data, usability studies, research, and market analysis into product strategies and requirements.
Communicate complex and technical concepts at the appropriate level and in the appropriate medium for each audience.
Align products with Meta's mission and strategic and competitive position.
Define and analyze product success metrics and track key performance indicators.
Drive decisions using user insights, quantitative analysis, and A/B testing.
Design sophisticated experiments, interpret results using AI, and make strategic product decisions.
Define and run evaluations to interpret model outputs at scale and establish evaluation as a strategic capability for AI-powered experiences.