AI-Driven Product Manager: Strategy & Growth

Meta; Menlo Park (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Meta is seeking a Product Manager for the Menlo Park, CA area to lead a complex product area within the AI-native initiative. You will partner with engineers, designers, data scientists and researchers to define vision, roadmap and success metrics.

The role emphasizes fast execution, prioritization, and evaluating AI tradeoffs, with a focus on scaling AI-enabled products and cross-functional collaboration.

Responsibilities

• Be responsible for leading and driving a complex product area; 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.
• 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.
• Critically evaluate when AI is (and isn't) the optimal solution, with sophisticated articulation of tradeoffs, risks, and second-order effects.
• Translate AI capabilities into compelling product visions that create differentiated value.
• Develop and champion AI-native strategies including comprehensive evals and data strategies that enable continuous improvement.
• 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, whilst improving team health and up-levelling the effectiveness of the team.
• Actively identify opportunities and resolve dependencies by creating shared strategies across teams in adjacent product areas.

Job description

Meta is seeking a Product Manager for the Menlo Park, CA area to lead a complex product area within the AI-native initiative. You will partner with engineers, designers, data scientists and researchers to define vision, roadmap and success metrics.

The role emphasizes fast execution, prioritization, and evaluating AI tradeoffs, with a focus on scaling AI-enabled products and cross-functional collaboration.