Product Manager, Central Security

Meta•Washington, DC

2d•$205,000 - $277,000

About The Position

Meta's products serve billions of people and businesses around the world, and it's our responsibility to keep their data secure. Meta's central security organization protects those people, the products they use every day, and the infrastructure behind them — while making it easier for the company to build and ship quickly. With security as a top company priority, we're growing our PM team to support cyber and information security at Meta. Our mission is to protect the company, our community, and its data while empowering safe innovation. We work across the full picture: securing the products people touch, the identity and access systems that guard accounts and data, the cryptography that protects information in transit and at rest, the detection and response capability that finds and shuts down threats, and the risk and compliance work that keeps us accountable to regulators worldwide. As we build PM and cross-functional capacity across a large organisation of security-focused engineers and partners spanning numerous products and platforms, we're focused on sustaining our strong collaboration and teamwork while developing long-term strategy and efficient, iterative execution for the security of our products, infrastructure, company information, and user data. Our PMs own security capabilities as products, not projects: defining strategy for a risk area, deciding where to invest against a threat landscape that shifts constantly, and building platforms that engineers adopt because they're genuinely the easiest path — secure by default rather than secure by mandate. You'll work with experienced security engineers, offensive security specialists, researchers, data scientists, legal and policy partners, and product teams across every part of the company, and you'll be measured on real risk reduction rather than features shipped.

Requirements

Demonstrated proficiency using AI-enabled tools to build product artifacts at scale
10+ years of experience working collaboratively with engineering, design and user research teams
10+ years product management and/or Product Design
Experience developing and championing AI-native strategies across organizations
Experience using data analysis and structured problem-solving to inform product strategy and prioritization decisions
Experience navigating through the full product life-cycle, integrating customer feedback into product requirements, driving prioritization, and pre- and post-launch execution
Experience presenting to executive audiences
BA/BS in Computer Science or related field

Nice To Haves

Experience across information security, cyber risk or data protection — gained in a technical, advisory or governance capacity
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience in a consumer-focused technology company
Track record of scaling AI best practices across organizations
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Responsibilities

Is the primary driver for identifying significant near and long-term opportunities in a large Product area, and driving product mission, strategies, and roadmaps in the context of broader organizational strategies and goals
Generate buy-in and drive consensus across organizations. Bring clarity and structure to ambiguous opportunities. Consistently demonstrate initiative and execute with limited oversight
Critically evaluate when AI is (and isn't) the optimal solution at portfolio level, setting the standard for rigorous tradeoff analysis
Translate AI capabilities into compelling, differentiated product visions that define market categories
Champion AI-native strategies including comprehensive evals and data strategies that enable org-wide continuous improvement
Drive product development with teams of engineers and designers, while maintaining team health
Work closely with cross-functional teams to drive product mission, define product requirements, coordinate resources from other groups (design, legal, etc.), develop roadmaps, and guide the team through key milestones
Reimagine workflows, responsibly using AI tools to transform team velocity and capability at organizational scale
Promote rapid experimentation and learning practices that become a competitive advantage
Scale AI best practices (including responsible AI use), workflows, and artifacts across the organization so capability compounds exponentially
Plan, initiate, and manage information technology projects for web-based products, applications, and platforms
Orchestrate complex execution across multiple organizations by combining AI automation with strategic human oversight at scale
Maximize efficiency in a constantly evolving environment where the process is fluid and creative solutions are the norm
Use AI-enabled tools to build products—setting the standard for PM technical capability
Interpret research and state-of-the-art learnings to design product strategy and apply rigorous logical reasoning at the frontier
Demonstrate expert understanding of system/architecture trade-offs and how they impact user experience and business outcomes; lead strategic technical decisions with engineering leadership
Integrate data, usability studies, research, and market analysis into product strategies and requirements to enhance user satisfaction and improve engineer productivity
Express complex and technical concepts at the right altitude, choosing the right medium and level of detail for the audience (XFN partners, execs, engineering, etc.)
Understand Meta's strategic and competitive position and deliver products that are aligned with our mission and recognized best in the industry
Define and analyze metrics that inform the success of products. Identify and track key performance metrics. Drive decision-making through user insights, quantitative analysis, and AB testing
Design sophisticated experiments and interpret results (leveraging AI) to drive strategic product decisions
Define and run evaluations (evals) to interpret model outputs at scale—establishing evaluation as a strategic capability for AI-powered experiences