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

Job description

**Overview** **About the Role** Microsoft Advertising serves ads across search, native, display, video, commerce, and emerging AI-powered experiences. Protecting these ecosystems requires decision systems that can understand advertisers, content, behavior, intent, landing pages, identities, and marketplace activity across every modality. We are seeking a **Principal Applied Science Manager** to lead the science organization responsible for risk, editorial quality, moderation, policy enforcement, and Responsible AI across Microsoft Advertising. The team will build the next generation of intelligent decision systems, including: - Foundation models for advertiser behavior and risk. - Foundation moderation models spanning text, image, video and multimodal experiences. - Deep-research agents that investigate complex cases, gather evidence, reason across signals, and assist automated and human decision-making. - Tiered enforcement systems that select the right model, workflow, and level of review based on risk, severity, confidence, latency, and cost. - Human-in-the-loop that combine advanced AI capabilities with expert judgment and accountability. - Continuous evaluation and audit systems that measure quality, fairness, safety, robustness, and business impact. This role owns both **scientific direction and production impact of the science**. The successful candidate will define the long-term strategy, build and lead a strong science team, and work closely with engineering, product, platform, policy, review operations, and partner organizations to ship these capabilities across all Microsoft Ads products. The role is ideal for a leader who can operate across multiple horizons: delivering measurable improvements today while shaping the future of Responsible AI, trust and safety, and intelligent enforcement systems. **Responsibilities** **Responsibilities** - 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 etc - Development 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 can investigate complex cases, retrieve and assess evidence, reason across multiple signals, identify contradictions, and support high-quality decisions. - Design tiered enforcement architectures that combine lightweight 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. **Qualifications** **Required Qualifications** - 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. - Demonstrated 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 one or more of the following: - Foundation models and large-scale representation learning. - Fraud, abuse, risk, trust and safety, or cybersecurity. - Content moderation, editorial quality, or policy enforcement. - Multimodal understanding across text, image, video, audio, and web content. - Agentic systems, retrieval, reasoning, and evidence-based decision 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 the 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. - Demonstrated ability to operate effectively in ambiguous and rapidly changing technical, regulatory, and Responsible AI environments. - Strong communication and executive-influence skills. **Preferred Qualifications** - Experience building foundation models for behavior understanding, moderation, risk, or trust and safety. - Experience with agentic sys