This role pays less than
56%
of similar roles. Most pay
$177,250–$263,937
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$221k
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
598 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 586 roles with salary data.
As a Principal Applied Scientist - Auction & Bidding on the Ads Monetization Algo team, you will focus on optimizing ad performance by bidding for advertisers in real-time auctions and designing long-term auction mechanisms. You will build and develop both online stacks and offline workflows to determine which ads enter auctions based on user and advertiser intent signals. Your daily work involves designing, implementing, and analyzing bidding strategies, prototyping auction mechanisms, and overseeing large-scale experiments using advanced statistics and machine learning. The role requires expertise in mathematical modeling, optimization theory, control theory, game theory, mechanism design, causal inference, and information retrieval. You will apply these skills to solve complex problems regarding marketplace health and advertiser return on investment within the specific domain of automated bidding systems and auction design for an advertising ecosystem.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Large Language Models
Information Retrieval
Ranking Systems
Agentic AI
Retrieval-Augmented Generation
Supervised Fine-Tuning
Preference Optimization
Model Distillation
Machine Learning
A/B Testing
Distributed Training
Search Engines
Machine Learning
Anomaly Detection
Large Language Models
Deep Learning
Causal Inference
Statistical Machine Learning
Data Mining
System Design
Low-latency Pipelines
Experimentation Frameworks
Model Operationalization