This role pays more than
58%
of similar roles. Most pay
$185,500–$237,787
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$212k
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
658 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 642 roles with salary data.
As a Principal Applied Scientist on the AI Transformation team, you will apply data science, machine learning, and generative AI to build intelligent solutions that transform internal operations. Working within multidisciplinary teams, you will translate ambiguous business problems into rigorous technical approaches, prototyping and iterating on systems that deliver measurable impact. Your daily work involves advancing state-of-the-art technologies including foundation models, reasoning systems, multi-agent architectures, and RAG to solve complex operational challenges. You will design robust evaluation methodologies, build scalable learning loops, and develop reusable frameworks to accelerate transformation across the organization. The role requires expertise in developing production-ready capabilities while ensuring responsible AI, privacy, and security standards are met. You will utilize AI-first development tools to move promising research into practical applications, focusing on creating AI-native operating models rather than just adding AI to existing processes.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 11 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
Python
Machine Learning
Retrieval-Augmented Generation
Large Language Models
Data Engineering
OCI
knowledge-graph s
Data Pipelines
Statistical Analysis
Model Evaluation
Experimental Design
Bioinformatics
Genomics