Join Microsoft Monetization to build the future of AI-powered monetization and shape how people discover, choose, and engage with products, services, and brands across Microsoft’s most innovative experiences.
Microsoft Monetization is building the next generation of AI-powered advertising and commerce experiences across Search, Shopping, Copilot, and emerging agentic surfaces. As conversational agents increasingly become the interface between users and businesses, we are reimagining how products, services, and ads are discovered, selected, personalized, and delivered.
Explore opportunities across our teams and find the role where your expertise can make the greatest impact.
Microsoft Search and Audience Network (MSAN)-Principal Applied Scientist
In this role you will:
- 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, influence architecture, research investments, model roadmaps, and execution strategy, and remain deeply hands-on in advancing machine learning innovation
Relevance and Ranking-Principal Applied Scientist
This team builds and improves machine learning models that directly shape the customer experience.
In this role you will:
- connect model performance to real-world impact by analyzing product and user data,
- understand how people interact with the system
- identify opportunities to elevate the experience and iterate quickly with product and engineering partners.
AI Experiences employees who live within a 50- mile commute of a designated Microsoft Hub in the U.S. are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
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.
Qualifications
- Required Qualifications:
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
- OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
- Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
- 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.
- 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.
Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
Applied Sciences IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
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.
Skills Required
- Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, plus 6+ years of related experience
- Master's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, plus 4+ years of related experience
- Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, plus 3+ years of related experience
- Equivalent experience may substitute for the stated education and experience requirements
- Master's degree in a related field plus 12+ years of related experience
- Doctorate in a related field plus 8+ years of related experience
- Extensive experience building and shipping large-scale machine learning systems
- Deep expertise in deep learning, transformers, representation learning, retrieval, recommendation systems, and foundation models
- Experience serving as a technical lead for large-scale cross-organizational initiatives
- Experience translating research innovations into production systems with measurable business impact
- 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
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
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Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
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Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.
Microsoft Insights
What We Do
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