Product Manager, Central Product

Meta•Seattle, WA

22h•$146,000 - $204,000

About The Position

Meta Product Managers work with cross-functional teams of engineers, designers, data scientists and researchers to build products. We are looking for Product Managers who value moving quickly and demonstrate AI-native product development practices.

Requirements

5+ years of relevant industry experience with at least 2 years in Product Management
Bachelor's degree (or relevant degree equivalent): STEM subject ideal but not essential (Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences)
Experience working with a cross-functional product team on a significant product area: Crafting product vision and strategy, defining product requirements, coordinating resources from other groups (marketing, legal, etc.), and driving the team to achieve key milestones and goals
Experience managing a product through multiple product life cycle phases
Proven experience to drive a material change in the performance of a product and the effectiveness of the team that delivers that product
Demonstrated experience to analyze large scale, complex data sets and making effective decisions based on data
Experience using AI-enabled tools to build product, prototypes, or other tangible product artifacts
Demonstrated ability to develop AI-native strategies including evals and data strategies
Experience integrating a diverse set of requirements from a broad set of users as well as context into a single coherent product strategy
Experience leading and motivating a product team and collaborating with partner teams
Demonstrated experience in communication, bringing extreme clarity to complex and technical messages at the appropriate level for the audience

Nice To Haves

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

Responsibilities

Responsible for leading a product area; defining success metrics, prioritizing product problems and identifying the best strategies for the product, aligned with the organizational goals.
Adapt and adjust your strategy to reflect learnings and changes in the product.
Critically evaluate when AI is (and isn't) the right solution for user problems, with clear articulation of tradeoffs and risks.
Translate AI capabilities into compelling product visions grounded in real user value.
Develop AI-native strategies including evals strategy and data strategy that enable iteration and measurable quality improvements.
Leverage AI for identifying opportunities through deep market research, user feedback synthesis, and competitive analysis.
Work with a cross-functional team to define a product, develop a roadmap and drive progress against goals and milestones and resolve challenges and blockers, while maintaining team health.
Reimagine workflows, responsibly using AI tools to increase personal and team velocity (e.g., faster synthesis, clearer decision docs, tighter iteration loops).
Foster a culture of rapid experimentation and learning, especially around AI-powered product development.
Scale AI best practices (including responsible AI use) and proficiency across product teams to multiply impact.
Provide mentorship and coaching to other Product Managers and cross-functional team members on AI-native practices.
Coordinate proactively across partner functions for product success.
Structure shared roadmaps for best outcomes.
Orchestrate complex execution across multiple workstreams by combining AI automation with appropriate human oversight.
Communicate product strategy and progress with clarity to all stakeholders.
Use AI-enabled tools to build products—prototyping, validating, or shipping tangible artifacts independently.
Interpret research and state-of-the-art learnings to design product strategy and apply logical reasoning.
Understand system/architecture trade-offs and how they impact user experience and business priorities and engage credibly with engineering partners on constraints and decisions.
Design experiments and interpret results (leveraging AI to accelerate analysis) and turn insights into concrete decisions.
Define and run evaluations (evals) to interpret model outputs and adjust execution based on learnings—treating evaluation as a first-class product practice.