This role pays more than
54%
of similar roles. Most pay
$185,307–$248,440
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$217k
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.
The Principal Applied Scientist joins the Content team to develop next-generation intelligent, large-scale content platforms for AI-driven experiences. This role involves leading applied science for grounding, search, retrieval, and ranking systems that power agentic AI across various surfaces. The individual will build and improve ranking models for search results, source selection, and tool choices while developing grounding systems to ensure reliable, source-backed answers from web content and enterprise data. Day-to-day responsibilities include tuning models using supervised fine-tuning, preference optimization, reward modeling, distillation, and synthetic data generation. The role requires expertise in large language models, information retrieval, and conversational AI. Key technical focuses include query understanding, semantic and hybrid retrieval, and multi-turn agent experiences involving planning and error recovery. The candidate will also define evaluation metrics for factuality, hallucination reduction, and citation correctness to ensure high-quality production outputs.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Machine Learning
Large Language Models (LLMs)
Deep Learning
Azure
AI Safety
Supervised Fine-Tuning
Distillation
Policy Optimization
Distributed Systems
Algorithms
Software Engineering
Predictive Analytics
Statistics
Data Science
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