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 ScientistThis 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.
ResponsibilitiesServe 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.
QualificationsBachelor'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.
Preferred Qualifications:- 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.
