Product Manager, Identity

Meta; City of Westminster; On-site; GBP 120,000 - 180,000; Full time

Job summary

Meta Product Managers work with cross-functional teams to build AI-native products at scale. You will define success, prioritise problems, and adapt strategies to evolving context.

You will translate AI capabilities into compelling visions, drive roadmaps, and collaborate across engineering, design, data science, and research to deliver high-impact outcomes. The role emphasises responsible AI, rapid experimentation, and mentoring other PMs while aligning with broader Meta objectives and

Qualifications

• 8+ years of relevant industry experience with at least 3 years in Product Management.
• Bachelor’s degree or equivalent in STEM or related field.
• Experience leading cross-functional product teams across a full product line.
• Experience managing a product through the full lifecycle.
• Proven ability to drive a step change in performance of a product and team.
• Extensive understanding of analyzing large-scale data sets and data-driven decisions.
• Proficiency using AI-enabled tools to build product artifacts.
• Experience developing and scaling AI-native strategies across teams.
• Experience integrating diverse requirements from users into a single product strategy.
• Strong communication across organization.

Responsibilities

• Identify priorities and align strategies with organisational context.
• Prioritize opportunities and decide when to start or stop investments.
• Evaluate when AI is the best solution, with trade-offs and risks.
• Translate AI capabilities into compelling product visions.
• Develop AI-native strategies and data plans for continuous improvement.
• Define product vision, roadmap, and drive progress with cross-functional teams.
• Resolve dependencies by creating shared strategies across teams.
• Reimagine workflows using AI to boost velocity and capabilities.
• Foster rapid experimentation and learning.
• Scale AI best practices across product teams.
• Mentor and coach other PMs and team members.
• Influence strategy across partner functions.
• Orchestrate execution across workstreams with AI and human oversight.
• Communicate product strategy clearly to stakeholders.
• Use AI-enabled tools to prototype, validate, or ship products.
• Interpret research and apply rigorous reasoning to strategy.
• Understand system/architecture trade-offs impact UX and outcomes.
• Lead credible technical discussions with engineering partners.
• Gather user research and market analysis to inform decisions.
• Design experiments and interpret results to drive product decisions.
• Define and run evals to interpret model outputs.

Skills

PM experience

Cross-functional leadership

AI product development

Data-driven decisions

Education

Bachelor’s degree

Tools

AI tools

Job description

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 lead AI-native product development at scale., 2. defining success, prioritizing problems and identifying the best strategies, considering wider organizational and company context. Adapt and adjust your strategies to reflect learnings and changes in context

Responsibilities

• 2. defining success, prioritizing problems and identifying the best strategies, considering wider organizational and company context. Adapt and adjust your strategies to reflect learnings and changes in context
• 3. Identify and drive consensus for how to prioritize and realize the most significant opportunities in your product area. Identify when to stop as well as when to start investment
• 4. Critically evaluate when AI is (and isn't) the optimal solution, with sophisticated articulation of tradeoffs, risks, and second-order effects
• 5. Translate AI capabilities into compelling product visions that create differentiated value
• 6. Develop and champion AI-native strategies including comprehensive evals and data strategies that enable continuous improvement
• 7. Work with a cross-functional team to define a product vision, develop a roadmap and drive progress against goals and milestones and resolve challenges and blockers, while improving team health and upleveling the effectiveness of the team
• 8. Actively identify opportunities and resolve dependencies by creating shared strategies across teams in adjacent product areas
• 9. Reimagine workflows, responsibly using AI tools to dramatically increase team velocity and capability
• 10. Foster a culture of rapid experimentation and learning across the organization
• 11. Scale AI best practices (including responsible AI use), workflows, and artifacts across product teams so the org's capability compounds over time
• 12. Support the growth of other PMs and cross-functional team members by providing mentorship and coaching on AI-native practices
• 13. Influence strategy and progress across partner functions, enhancing collaboration and resolving divergent goals
• 14. Orchestrate complex execution across multiple workstreams by combining AI automation with strategic human oversight-using AI to reduce toil while maintaining high quality and accountability
• 15. Communicate product strategy and progress clearly and concisely to all stakeholders
• 16. Use AI-enabled tools to build products-independently creating tangible artifacts to prototype, validate, or ship
• 17. Interpret research and state-of-the-art learnings to design product strategy and apply rigorous logical reasoning
• 18. Demonstrate deep understanding of system/architecture trade-offs and how they impact user experience and business outcomes
• 19. lead credible technical discussions with engineering partner
• 20. Gather and analyze user research and market analysis to inform product decisions and influence the wider product organization
• 21. Design sophisticated experiments and interpret results (leveraging AI to accelerate analysis) and drive concrete product decisions
• 22. Define and run evaluations (evals) to interpret model outputs and adjust execution based on learnings-establishing evaluation as a first-class product practice across teams

Qualifications

• 23. 8+ years of relevant industry experience with at least 3 years in Product Management
• 24. Bachelor's degree (or relevant degree equivalent): STEM subject ideal but not essential (Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences)
• 25. Experience leading cross-functional product team(s) across a full product line: Crafting product mission and strategy, defining product requirements, coordinating resources from other groups (marketing, legal, etc.), and driving the team to achieve key milestones and goals
• 26. Experience managing a product through the full product lifecycle
• 27. Proven experience to drive a step change in the performance of a product and the effectiveness of the team that delivers that product
• 28. Extensive understanding in analyzing large-scale, complex data sets and making effective decisions based on data
• 29. Demonstrated proficiency using AI-enabled tools to build product artifacts
• 30. Experience developing and scaling AI-native strategies including evals and data strategies across teams
• 31. Experience integrating a diverse set of requirements from a broad set of users as well as context into a single coherent product strategy
• 32. Experience leading and motivating teams and influencing across an organisation
• 33. Demonstrated experience in communication, bringing extreme clarity to complex and technical messages across all audience levels
• 34. Experience identifying and hiring the right talent to build an AI-capable cross-functional team
• 35. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• 36. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 37. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)