Principal Applied Scientist

Microsoft•Redmond, WA

21h•$165,600 - $331,200

About The Position

The Principal Applied Scientist will work on Microsoft’s Core Search and AI team (Bing), developing next-generation search capabilities using advanced AI technologies, particularly large language models, at scale. The team builds large-scale machine learning systems for search scenarios including search-result quality, query and document understanding, retrieval and reranking, and AI search grounding. Its search experiences serve more than 500 million monthly active users through Bing and other search engines. The role leads major research and product initiatives, applying advanced research methodologies and machine learning expertise to improve search relevance and deliver measurable business impact. It advances LLM post-training and alignment, retrieval and ranking systems, and retrieval-augmented generation (RAG) pipelines, translating research into reliable production improvements through rigorous experimentation and collaboration with engineering, research, product, and design partners. The scientist also contributes to team development through mentoring, onboarding, academic engagement, and sharing research findings. Starting January 26, 2026, AI Experience employees living within a 50-mile commute of a designated U.S. Microsoft office, or within a 25-mile commute of a designated office outside the U.S., are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Requirements

Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, and 8+ years of related experience; or a master’s degree in one of these fields and 6+ years of related experience; or a doctorate in one of these fields and 5+ years of related experience; or equivalent experience.
6+ years of coding experience in Python, C++, C#, C, or Java.
6+ years of industry experience applying machine learning techniques.
Experience building and improving large-scale machine learning systems for search, ads, or recommendation, adopting LLMs.
Research background in machine learning, LLMs, and NLP.
Ability to identify and solve novel problems that have not previously been solved.

Nice To Haves

Master’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 12+ years of related experience; or a doctorate in one of these fields and 8+ years of related experience; or equivalent experience.

Responsibilities

Lead one or more major initiatives using expertise across a broad research landscape, including advanced research methodologies and applied techniques.
Develop deep knowledge of a service, platform, or domain and identify product opportunities by sharing emerging industry trends and applied technologies.
Review business requirements and incorporate research insights to meet strategic goals.
Direct data and machine learning model strategies for complex problems and apply subject-matter expertise to achieve measurable business impact.
Support onboarding of new team members and help academic collaborators become effective contributors in multidisciplinary teams.
Identify promising research talent, engage with the academic community, and strengthen Microsoft’s long-term recruiting pipeline.
Advance LLM post-training and alignment techniques by designing, implementing, and evaluating methods that improve reasoning quality, safety, controllability, and factual grounding in large-scale models.
Build and optimize retrieval, ranking, and relevance systems that integrate with LLM-powered experiences to develop next-generation search capabilities.
Architect and refine RAG pipelines to improve retrieval fidelity, reduce hallucinations, and deliver context-aware, user-aligned responses in production.
Translate research into production by running experiments, analyzing results, and collaborating with engineering partners to deploy scalable, reliable model improvements.
Use hypothesis-driven experimentation, reproducible methodologies, and clear documentation to promote scientific rigor and communicate findings, insights, and model behaviors.
Collaborate across research, engineering, product, and design to shape long-term strategy for search, alignment, and retrieval-augmented systems.
Share learnings, mentor peers, and participate in internal research discussions, reviews, and technical deep dives.