Applied Sciences IC6

Microsoft•Redmond, WA

About The Position

The Core Search and AI team at Microsoft is seeking a Principal Applied Scientist to advance next-generation search using large language models and other advanced AI technologies. The team develops large-scale machine learning systems for search scenarios including document retrieval, query and document understanding, retrieval and reranking, search relevance, and AI search grounding. Its work supports Bing and dependent search engines, reaching more than 500 million monthly active users worldwide. The role leads major initiatives across applied research and product development. It applies machine learning expertise to improve search relevance, retrieval, ranking, LLM post-training and alignment, and retrieval-augmented generation (RAG). The scientist translates research into production through rigorous experimentation and collaboration with engineering, product, design, and research partners, while contributing to technical strategy, mentoring, and recruiting. Starting January 26, 2026, AI Experience employees who live within a 50-mile commute of a designated U.S. Microsoft office, or within a 25-mile commute of a designated non-U.S. office, 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.
Proficient problem-solving ability, including identifying and solving problems that have not been solved before.

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.
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 and adopting LLMs.
Research background in machine learning, LLMs, and NLP.

Responsibilities

Lead one or more major initiatives using advanced research methodologies and applied techniques.
Develop deep knowledge of a service, platform, or domain and identify product opportunities based on industry trends and applied technologies.
Review business requirements and incorporate research insights to meet strategic goals.
Set direction on data and machine learning model strategies for complex problems, applying 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.
Design, implement, and evaluate novel LLM post-training and alignment methods to improve reasoning quality, safety, controllability, and factual grounding at scale.
Build and optimize retrieval, ranking, and relevance systems integrated with LLM-powered experiences.
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.
Promote scientific rigor through hypothesis-driven experimentation, reproducible methodologies, and clear documentation of 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.