Principal Applied Scientist - Machine Learning & Recommender Systems

Principal Applied Scientist

Overview

Microsoft Advertising is building the next generation of intelligent recommender systems that power how users discover relevant ads and how advertisers connect with customers across search, native, display, video, and emerging AI experiences. 

We are seeking a Principal Applied Scientist to lead high-impact applied science initiatives across large-scale recommender systems in Ads. This role is centered on retrieval, matching, marketplace optimization, ad-quality, fraud and abuse detection, content understanding, and emerging foundation-model-powered experiences. The role  involves shaping and applying LLMs, large retrieval models, multimodal systems, and agentic or deep-research-style workflows to high-impact advertising scenarios at scale.

This is an opportunity to define technical direction on frontier machine learning problems with direct production impact : improving relevance, revenue, quality, safety, efficiency, and advertiser outcomes at massive scale.

Responsibilities

Responsibilities

• Lead the design and development of machine learning models and algorithms for large-scale recommendation in Ads.
• Define and drive the applied science roadmap for recommender systems that improve relevance, engagement, marketplace quality, efficiency, and business impact.
• Shape technical strategy across multiple horizons, balancing near-term product wins with long-term investments in modern AI approaches.
• Drive cross-team scientific leadership by partnering with  product, platform, and partner science teams to drive innovations and also push them from research into production at scale.
• Mentor senior scientists and engineers, raise the technical bar across the organization, and contribute to a culture of scientific rigor, innovation, and engineering excellence.
• Represent the organization through technical reviews, publications,  and strategic cross-company collaboration.

Qualifications

Required Qualifications

• Bachelor’s Degree in Computer Science, Statistics, Electrical Engineering, Computer Engineering, or related field AND 10+ years of related experience in applied science, machine learning, recommender systems, information retrieval, ranking, or related areas
  OR Master’s Degree in a related field AND 8+ years of related experience
  OR Doctorate in a related field AND 5+ years of related experience
  OR equivalent experience.
• Deep expertise in one or more of the following: retrieval, ranking , marketplace optimization,  large-scale machine learning.
• Proven experience building, shipping, and scaling machine learning models in production environments with measurable business impact.
• Strong communication, technical leadership, and cross-functional collaboration skills.

Preferred Qualifications

• Experience in recommendation systems,  or other large-scale ml driven online platforms.
• Experience training and adapting SLMs and large retrieval models, including pre-training and post-training.
• Familiarity with large-scale distributed training, online inference systems, and latency-sensitive production services.

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.

Experience Level

Senior Level