Principal Applied Science Manager - Foundation Models, Agents & Trust Systems

What you'll do

• Define and drive the multi-year science strategy for risk, editorial quality, moderation, policy enforcement, and Responsible AI across Microsoft Advertising
• Build, lead, and grow a high-performing team of applied scientists working across foundation models, multimodal understanding, behavior modeling, agentic systems
• Develop foundation behavior models that understand advertisers, accounts, domains, identities, payments, content, and activity over time
• Develop foundation moderation models that generalize across policies, products, languages, markets, and modalities
• Build Deep Research agents that investigate complex cases, retrieve and assess evidence, reason across multiple signals, identify contradictions, and support high-quality decisions
• Design tiered enforcement architectures combining classifiers, specialized models, foundation models, agents, deterministic systems, and human review
• Determine when decisions should be automated, escalated to advanced models or agents, or routed to expert human reviewers
• Establish scientific foundations for risk scoring, severity estimation, uncertainty, calibration, explainability, and cost-sensitive decision-making
• Drive measurable improvements in user safety, marketplace integrity, advertiser experience, decision quality, operational efficiency, and revenue protection
• Evolve scientific and engineering approaches as Responsible AI expectations, adversarial behaviors, policies, and model capabilities change
• Work across product management, platform engineering, review operations, policy, legal, privacy, Responsible AI, and partner science organizations
• Influence senior leaders on scientific strategy, platform architecture, organizational investments, technical priorities
• Mentor senior scientists and managers, raise scientific standards, and build the next generation of applied-science leadership

Requirements

• Bachelor’s degree in Computer Science, Statistics, Electrical Engineering, Computer Engineering, or a related field and 15+ years of relevant experience; or a Master’s degree and 12+ years of relevant experience; or a Doctorate and 10+ years of relevant experience; or equivalent experience
• Experience leading applied-science or machine-learning teams and developing senior technical talent
• Proven track record of defining scientific and product strategy and translating it into large-scale production capabilities with measurable customer, business, and operational impact
• Ability to make complex product and technical trade-offs across quality, coverage, latency, cost, explainability, safety, and speed of delivery
• Deep expertise in foundation models, fraud/abuse/risk/trust and safety/cybersecurity, content moderation, editorial quality, policy enforcement, multimodal understanding, agentic systems, large-scale classification, ranking, recommendation, or decision systems
• Experience leading complex initiatives across engineering, product, operations, policy, and partner science organizations
• Ability to lead productionization of complex machine-learning systems including data and labeling strategy, experimentation, model evaluation, deployment architecture, observability, reliability, latency, capacity, cost, and operational readiness
• Ability to connect scientific advances with product requirements, operational workflows, engineering constraints, and business outcomes
• Ability to operate effectively in ambiguous and rapidly changing technical, regulatory, and Responsible AI environments
• Strong communication and executive-influence skills

Tech stack