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Product Manager Lead, Integrity Measurement

Meta
2 days ago
Full-time
On-site
Menlo Park, California, United States
$205,000 - $277,000 USD yearly
The Integrity Measurement team sets the foundation for how we think about and operate Integrity across Meta’s product suite. This team is responsible for developing the right metrics, building and maintaining these at scale, and leveraging these to enable clear decision making and prioritization as user behavior and our products themselves evolve. We’re at a unique moment where AI enables us to rethink our approach to measurement - novel techniques for AI-first metrics, more granularity, and greater agility than ever before. This is a cross-cutting role leading the Measurement team and product through this transition, working closely with partners across Meta. The ideal candidate has strong product intuition, comfort with AI, and deep statistical expertise.

Vision & Strategy: Establish shared vision across the company by building consensus on priorities leading to product execution.

Understand Meta's strategic and competitive position and deliver products that are recognized best in the industry.

Set the standard for critically evaluating when AI is (and isn't) the optimal solution across product portfolios.

Champion AI-native strategies including comprehensive evals and data strategies that enable org-wide excellence.

Reimagine workflows, responsibly using AI tools to transform team velocity and capability at organizational scale.

Leadership & Collaboration: Lead a team through the ideation, technical development, and launch of innovative products.

Drive product development with a team of world-class engineers and designers.

Attract, build, manage, and develop a talented team of product managers and product leaders with a broad range of experiences, perspectives, approaches, and backgrounds—with emphasis on AI-native capabilities.

Foster a culture of rapid experimentation and learning that becomes a competitive advantage for the organization.

Scale AI best practices (including responsible AI use), workflows, and artifacts across teams so organizational capability compounds over time.

Execution & Prioritization: Orchestrate complex execution across multiple teams and organizations by combining AI automation with strategic human oversight.

Maximize efficiency in a constantly evolving environment where the process is fluid and innovative solutions are the norm.

Technical / Analytical / Design: Set expectations for PM technical capability, including use of AI-enabled tools to build products.

Integrate usability studies, research and market analysis into product requirements to enhance user satisfaction.

Ensure teams understand system/architecture trade-offs and how they impact user experience and business outcomes.

Communication: Express complex and technical concepts at the right altitude, choosing the right medium and level of detail for executive, engineering, and cross-functional audiences.

Measurement & Impact: Define and analyze metrics that inform the success of products.

Establish evaluation (evals) as a first-class product practice across teams for AI-powered experiences.

Drive decision-making through user insights, quantitative analysis, and AI-accelerated experimentation.

8+ years of experience in Product Management and/or Product Design

8+ years of experience working collaboratively with engineering, design and user research teams

Critical thinking/analytical leadership experience

Experience presenting to executive audiences

6+ years of experience hiring, managing, and developing both individual contributors and leaders—with demonstrated ability to build AI-capable teams

Demonstrated proficiency with AI-enabled tools and ability to set AI-native PM standards across organizations

BA/BS in Computer Science or related field

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