Principal Applied Scientist-Ads Monetization

Microsoft•Redmond, WA

18h•$142,800 - $331,200

About The Position

Microsoft Monetization is building AI-powered advertising and commerce experiences across Search, Shopping, Copilot, and emerging agentic surfaces. The Principal Applied Scientist will set the science vision and technical strategy for relevance and ranking, user data and intent understanding, personalization, recommendation, and agent-driven commerce. The role leads across science areas, influencing architecture, research investments, model roadmaps, and execution strategy while remaining hands-on in machine learning innovation. The position serves as technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads. It leads end-to-end machine learning development and partners with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems. The scientist shapes future agent experiences, conversational shopping, and AI-assisted commerce, drives measurable customer and business outcomes, and mentors scientists and engineers.

Requirements

Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience, or a master's degree in one of these fields and 4+ years of related experience, or a doctorate in one of these fields and 3+ years of related experience, or equivalent experience.
Extensive experience building and shipping large-scale machine learning systems in search, recommendation, ranking, advertising, commerce, conversational AI, or personalization.
Deep expertise in modern machine learning, including deep learning, transformers, representation learning, retrieval systems, recommendation systems, and foundation models.
Demonstrated experience serving as a technical lead for large-scale cross-organizational initiatives.
Proven ability to translate research innovations into production systems with measurable business impact.

Nice To Haves

Master's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 12+ years of related experience, or a doctorate in one of these fields and 8+ years of related experience, or equivalent experience.
Experience with LLMs, SLMs, multimodal AI, and agentic systems.
Experience in advertising, e-commerce, shopping, recommendation, or marketplace ecosystems.
Experience developing AI-powered assistants, commerce experiences, or personalization platforms.
Experience optimizing distributed training and inference systems on large GPU clusters.
Experience mentoring principal-level engineers, scientists, and technical leaders.

Responsibilities

Set the science vision and technical strategy for relevance and ranking, user data and intent understanding, personalization, recommendation, and agent-driven commerce.
Lead across multiple science areas and influence architecture, research investments, model roadmaps, and execution strategy while remaining hands-on in advancing machine learning innovation.
Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives across Copilot, Shopping, and Ads experiences.
Drive innovation in machine learning technologies, including LLMs, SLMs, multimodal AI, retrieval, ranking, personalization, and recommendation systems.
Define and execute the science roadmap for user intent understanding, product understanding, content relevance, and advertiser matching.
Lead end-to-end ML development, including model architecture, training data strategy, evaluation, experimentation, calibration, and production deployment.
Partner with engineering and product teams to deliver scalable, reliable, and cost-efficient AI systems.
Shape the technical vision for future agent experiences, conversational shopping, and AI-assisted commerce scenarios.
Drive measurable improvements in customer satisfaction, engagement, relevance quality, and business outcomes.
Mentor scientists and engineers while raising the technical bar across machine learning, experimentation, and scientific rigor.
Analyze product and user data, understand how people interact with the system, identify opportunities to improve the experience, and iterate with product and engineering partners.