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

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. 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; 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