Principal Applied Science Manager - Foundation Models, Agents & Trust Systems
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. 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 Bachelor’s degree in Computer Science, Statistics, Electrical Engineering, Computer Engineering, or a related field and 15+ years of relevant 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. Preferred Qualifications Experience building foundation models for behavior understanding, moderation, risk, or trust and safety. Experience with agentic systems, tool-using agents, deep-research workflows, retrieval, structured reasoning, and evidence-based decision systems. Experience designing multi-stage or tiered model architectures that balance accuracy, latency, coverage, and cost. Background in advertising, search, commerce, recommendations, financial risk, cybersecurity, or another high-scale marketplace domain.