Principal Product Manager, Personalization AI

Job ID: 200046918Posted: 2026-08-06Location: United States, Washington, RedmondSalary: USD $165,600 - $296,400 per yearEmployment type: Full-TimeWork site: 4 days / week in-officeRole type: Individual ContributorTravel: Less than 25%Profession: Product ManagementDiscipline: Product ManagementCompany: MicrosoftOverviewMicrosoft AI’s content team is building the next generation of content with user understanding, and personalization systems behind experiences across Edge, Windows, Copilot, and partner surfaces that reach hundreds of millions of people. We are hiring a Principal Product Manager to drive personalization, user understanding and how that translates to our content ranking systems. You will define personalization and ranking strategy end to end: how user interests are represented, how candidates are retrieved, how content understanding and user signals become ranking features, how models are trained and evaluated, and how the final ranked experience trades off relevance, freshness, quality and long-term user value. You will set the vision for AI-native personalization, i.e. generative recommendation, agentic user understanding, and closed learning loops while also coordinating with our content ranking product managers. This role sits at the intersection of consumer product, ML systems of rigor, AI-native workflows and organizational leadership. You’ll operate with high agency and set directions that multiple teams execute against and align senior stakeholders across MAI and partner organizations. ResponsibilitiesDefine the product vision, strategy, and roadmap for AI-powered personalization across recommendation systems, user and content understanding, agentic memory, and generative AI content experiences.Translate user and product goals into clear requirements for ranking, retrieval, profile generation, memory, data pipelines, evaluation, and serving systems.Own communication with senior MAI leadership on overall strategy and investment.Own the ranking objective and how it integrates personalization: decide what models optimize for, balancing engagement, retention, content diversity, safety, and long-term user value, and translate that into label strategies, training data requirements, and model plans.Drive how signals become ranking features, including user behavior, content understanding, quality, freshness, and context. Work cross functionally to set quality bars for what enters the feature store and the models.Design how Edge, Windows, Copilot, and partner surfaces consume the shared ranking stack through clear interfaces, per-surface tuning, and quality bars that scale integration.Own the evaluation and experimentation framework: offline metrics that predict online outcomes, A/B test design, guardrail metrics, and proving ranking lift on engagement, retention, and business outcomes.Set strategic direction for the personalization platform — user profiles, agentic memory, content understanding, generative recommendation, and serving infrastructure — ensuring compounding capability over time.Align VP-level stakeholders across MAI and partner organizations, set prioritization frameworks, and influence resource allocation across multiple teams.Partner with privacy, consent, legal, and policy teams to make ranking trustworthy by design.QualificationsRequired: Bachelor’s Degree AND 15+ years experience in product/service/program management or software development, OR equivalent experience. Preferred: 17+ years experience in product/service/program management or software development, OR equivalent experience. 7+ years building consumer-facing recommendation, personalization, search, feed, or content ranking products at scale. Track record of defining and landing product strategy at the organizational level, influencing investment decisions and aligning senior leadership behind a technical product vision. Experience owning ranking or recommendation objective functions end to end — deciding what models optimize for, not just how they optimize. Deep experience with ranking and personalization platform architecture: user profiles, candidate retrieval, ranking models, signal pipelines, feature stores, evaluation systems, and multi-surface integration. Experience with generative AI, LLMs, agents, user memory, or retrieval and ranking systems in production consumer products. Strong technical fluency across ML systems, ranking architectures, data pipelines, experimentation platforms, and scalable serving infrastructure. The combination of consumer product taste, systems rigor, and organizational leadership — caring about both the quality users feel and the strategy that compounds value over time. Working knowledge of SQL, Python, experimentation platforms, analytics tools, or data investigation workflows.  #MicrosoftAI Product Management IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.