Principal Applied Science Manager - Foundation Models, Agents & Trust Systems at Microsoft | AdTechTalent
Data Science1 month agoOn-site
Microsoft
Principal Applied Science Manager - Foundation Models, Agents & Trust Systems
foundation modelsmachine learningapplied scienceriskcontent moderationpolicy enforcementResponsible AImultimodalagentic systemsadvertisingtrust and safetylarge-scale systems
Key details
Salary
Not specified
Employment type
Full-time
Seniority
Lead
Years experience
10+
Location
Bengaluru, India
Full job description
Microsoft Advertising seeks a Principal Applied Science Manager to lead the science organization responsible for risk, editorial quality, moderation, policy enforcement, and Responsible AI. The role involves defining long-term scientific strategy, building and leading a team of applied scientists, and collaborating with engineering, product, platform, policy, and partner organizations. Responsibilities include developing foundation models for advertiser behavior and moderation, building deep research agents, designing tiered enforcement systems, and driving improvements in safety, integrity, and operational efficiency. Candidates must have a Bachelor’s degree or higher with 10+ years of relevant experience, demonstrated leadership in applied science or machine learning teams, expertise in foundation models, risk, content moderation, multimodal understanding, and the ability to manage complex machine-learning production systems. Strong communication and executive influence skills are required.
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
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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
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
foundation modelslarge-scale representation learningmachine learningmultimodal understandingagentic systemsretrievalreasoningevidence-based decision systemsclassificationrankingrecommendation systems
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