This role pays less than
58%
of similar roles. Most pay
$187,850–$260,450
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$224k
for comparable roles.
Based on 240 similar postings.
Employer
About Microsoft
Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing
Microsoft currently has
557 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 537 roles with salary data.
As a Principal Applied Scientist-Ads Monetization within the Microsoft Monetization team, you will serve as a technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives. You will define the science vision and roadmap for user intent understanding, product understanding, content relevance, and advertiser matching across Copilot, Shopping, and Ads experiences. Your daily work involves leading end-to-end machine learning development, including model architecture, training data strategy, evaluation, and production deployment. You will drive innovation in technologies such as LLMs, SLMs, multimodal AI, retrieval, ranking, and personalization systems. The role focuses on solving complex problems in conversational shopping and AI-assisted commerce by translating research into scalable, cost-efficient systems. Required expertise includes deep learning, transformers, representation learning, and experience with distributed training on large GPU clusters within advertising and e-commerce ecosystems.
What does a Applied Scientist earn?
Median $231000 from 37 postings across 11 companies.
Serve as the technical lead for AI-powered relevance, shopping, recommendation, and agentic commerce initiatives.
Drive innovation in machine learning technologies including LLMs, SLMs, multimodal AI, and retrieval systems.
Define and execute the science roadmap for user intent, product understanding, and advertiser matching.
Lead end-to-end ML development including architecture, training data strategy, evaluation, 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 and conversational shopping scenarios.
Drive measurable improvements in customer satisfaction, engagement, and business outcomes.
Mentor scientists and engineers while raising the technical bar for machine learning and experimentation.
What we're looking for
Bachelor's degree in a relevant field and 6+ years of experience in statistics, predictive analytics, or research.
Master's degree in a relevant field and 4+ years of experience in statistics, predictive analytics, or research.
Doctorate in a relevant field and 3+ years of experience in statistics, predictive analytics, or research.
Equivalent experience to the specified educational and experience requirements.
Extensive experience building large-scale machine learning systems in search, recommendation, ranking, advertising, commerce, or conversational AI (preferred).
Deep expertise in modern machine learning including deep learning, transformers, retrieval systems, and foundation models (preferred).
Experience with LLMs, SLMs, multimodal AI, agentic systems, and optimizing distributed training on large GPU clusters (preferred).
Demonstrated experience as a technical lead for cross-organizational initiatives and mentoring senior technical leaders (preferred).
Computer Vision
Machine Learning
Deep Learning
Multimodal AI
Video Processing
Media Systems
Large-scale Inference Platforms
Model Optimization
Edge Inference
GPU Acceleration
Predictive Analytics
Evaluation Frameworks
Productization
Real-time Media Systems