This role pays less than
52%
of similar roles. Most pay
$205,000–$255,600
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$230k
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 571 roles with salary data.
Principal Applied Scientist
The Principal Applied Scientist joins the MSAI Search Relevance team to deliver advanced machine learning and information retrieval solutions for enterprise-scale search. This role involves developing mission-critical innovations for Copilot experiences, including agentic search in orchestrator architectures and improving query understanding through deep language analysis. You will design and train relevance models using LLM fine-tuning and learning-to-rank approaches while building robust evaluation pipelines involving offline metrics and A/B testing. The work focuses on solving complex challenges in Copilot-driven enterprise search by implementing dense retrieval, hybrid search, and transformer-based re-ranking models. Key technologies include FAISS, ANN, ScaNN, and RAG architectures utilizing LLMs like GPT, T5, or Llama. You will also mentor a high-performing team, translate business goals into scientific strategy, and collaborate across organizations to improve retrieval for products like Outlook, Teams, and SharePoint.
What does a Applied Scientist earn?
Median $231000 from 40 postings across 11 companies.
Lead end-to-end applied science projects from initial ideation and design through implementation and production shipping.
Develop advanced retrieval and ranking models using LLM fine-tuning, RAG architectures, and learning-to-rank techniques.
Implement sophisticated search technologies including dense retrieval, hybrid search, and agentic search for enterprise-scale systems.
Build robust evaluation pipelines utilizing offline metrics and online A/B experimentation to measure search quality.
Mentor and manage a team of applied scientists by overseeing their technical direction and career development.
Translate ambiguous business requirements into clear, data-driven scientific strategies and technical roadmaps.
Document and share experimental results and methodologies to promote knowledge sharing across the organization.
What we're looking for
Bachelor's degree plus 6 years experience, Master's plus 4 years, or Doctorate plus 3 years in a relevant technical field.
Ability to pass Microsoft Cloud background checks and other required security screenings.
Experience in search and ranking systems, semantic retrieval, and information retrieval at scale (preferred).
Applied experience with state-of-the-art sparse and dense retrieval and ranking techniques (preferred).
Experience with RAG architectures using LLMs such as OpenAI GPT, T5, or Llama (preferred).
Experience with fine-tuning or prompt engineering of large-scale language models for query rewriting, summarization, and document reranking (preferred).
Familiarity with hybrid search strategies, learning-to-rank frameworks, and evaluation methodologies for IR systems (preferred).
Experience in data analysis at scale, including working with logs, telemetry, and large datasets (preferred).
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