Overview

Microsoft Advertising is building the next generation of intelligent recommendation and decisioning systems across search, native, display, video, commerce, and emerging AI-mediated experiences.

We are seeking a Senior Principal Applied Scientist to define and drive the scientific direction for large-scale recommender and decisioning systems across Ads. This role spans retrieval, matching, relevance, ad quality, fraud and abuse detection, content and intent understanding, foundation-models and its powered recommendation experiences.

This role is focused on creating the scientific foundations that enable new monetization scenarios to scale with high quality, operational efficiency, and rapid iteration across evolving product surfaces. The ideal candidate will operate across multiple horizons: delivering measurable near-term product and business impact, while shaping the long-term architecture of next generation of Ads systems.

Responsibilities

• Define, drive and deliver multi-year technical strategy, and investment roadmap for large-scale recommender and decisioning systems across Ads.
• Lead the invention, incubation, and productionization of next-generation machine learning systems spanning large retrieval models, foundation-model-powered recommendation systems and agentic workflows.
• Drive step-function improvements in relevance, user value, advertiser outcomes and system efficiency
• Influence senior leaders across product, engineering and partner science organizations on scientific direction, architecture, long-range capability building, and prioritization of major investments.
• Mentor scientists and engineers, elevate scientific rigor and technical standards across the organization, and help shape the next generation of applied science leadership.

Qualifications

• Bachelor’s Degree in Computer Science, Statistics, Electrical Engineering, Computer Engineering, or related field AND 15+ years of related experience in applied science, machine learning, recommender systems, information retrieval, ranking, optimization, or related areas
  OR Master’s Degree in a related field AND 12+ years of related experience
  OR Doctorate in a related field AND 10+ years of related experience
  OR equivalent experience.
• Deep expertise in one or more of the following areas: retrieval, relevance, recommender systems, large-scale machine learning, trust and safety
• Proven track record of defining technical and scientific strategy for large-scale machine learning systems and delivering measurable product and business impact in production environments.
• Demonstrated experience leading cross-organizational scientific initiatives and influencing technical direction across multiple teams, product surfaces, or organizational boundaries.
• Strong communication, technical leadership, and cross-functional collaboration skills, with the ability to connect deep scientific work to product, engineering, and business outcomes.
• Demonstrated ability to operate effectively in ambiguous, fast-evolving technical spaces and convert emerging opportunities into scalable production direction.
  Experience building foundation models for large-scale recommender systems

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.