Member of Product Staff, Ads

Responsibilities

• Lead a product area; define success metrics, identify measurement opportunities, build metrics frameworks, prioritize product problems, and identify 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.
• Maintain consistent UX quality by knowing when tool-generated output is sufficient vs. when specialist designer input is needed for production-quality craft.
• Produce strategic product visions that define market categories supported by directional design artifacts and prototypes.
• 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.
• 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.
• 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.
• Partner with Data Science on complex experiment design and setup.
• Create interactive prototypes using modern tools sufficient for user testing and engineering specs.
• Rapidly generate multiple design explorations during planning and roadmap sessions.

Qualifications

• 5+ years of relevant industry experience with at least 2 years in Product Management.
• Experience with revenue measurement, experimentation at scale, user modelling.
• Understanding of auction dynamics, advertiser demand signals, ranking and delivery mechanisms, and revenue/engagement trade-offs.
• 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.
• 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 make 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.
• Proven experience leading and motivating a product team and collaborating with partner teams.
• 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.