Principal Applied Scientist

Microsoft•Redmond, WA

1d•Hybrid

About The Position

Core Search and AI team (Bing) is looking for people who want to build the next generation of search using advanced AI technologies, especially large language models, at scale. We are responsible for the largest machine learning models at Microsoft by volume and take pride in being the first in the world to solve many practical AI at Scale challenges. Our work spans a very large scope of scenarios including delivering high quality search results from a massive document corpus, query and document understanding, retrieval and reranking model for search results optimization, as well as AI search grounding, etc. We are seeking a highly motivated and experienced Principal Applied Scientist with solid machine learning expertise and can adopt state-of-art AI technologies to improve the relevance for the next generation of search. As a team, we leverage the diverse backgrounds and experiences of passionate engineers, scientists, and program managers to help us realize our goal of making the world smarter and more productive. We believe great products are built by inclusive teams of customer-obsessed individuals who trust each other and work together closely. We collaborate regularly across the company to find technological breakthroughs from groups like Microsoft Research to infusing AI into the rest of Microsoft products like Office and Azure. Microsoft's mission is to empower every person and every organization on the planet to achieve more, and we believe that artificial intelligence will play a critical role in accomplishing that mission. The Core Search and AI team is the leading applied machine learning team at Microsoft responsible for delivering the highest-quality search experience to over 500M+ monthly active users around the world in Microsoft’s search engine, Bing and other dependent search engines such as Yahoo, DuckDuckGo, and new startups like Neeva. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location 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 related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
6+ years of experience coding in Python, C++, C#, C or Java.
6+ year of industry experience applying Machine Learning techniques.
Experience building and improving large scale Machine Learning system for search, ads, and recommendation, adopting LLM.
Research background on Machine Learning, LLM and NLP.
Proficient problem solver: ability to identify and solve problems that the world has not solved before.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Responsibilities

Lead one or more big initiatives leveraging expertise across a broad research landscape, including advanced research methodologies and applied techniques.
Gain deep knowledge of a service, platform, or domain, and drive product opportunities by sharing emerging industry trends and applied technologies.
Review business requirements and incorporate research insights to meet strategic goals.
Provide directions on the types of data and machine learning model strategies needed to solve complex problems and apply deep subject-matter expertise to drive measurable business impact.
Support the onboarding of new team members and help develop academic collaborators into effective contributors within 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 novel methods that improve reasoning quality, safety, controllability, and factual grounding across large-scale models.
Drive and develop next-generation search capabilities by building and optimizing retrieval, ranking, and relevance systems that integrate deeply with LLM-powered experiences.
Architect and refine RAG pipelines that enhance retrieval fidelity, reduce hallucinations, and deliver more context-aware, user-aligned responses in production environments.
Translate research into production by running experiments, analyzing results, and collaborating with engineering partners to deploy scalable, reliable model improvements.
Drive scientific rigor through hypothesis-driven experimentation, reproducible methodologies, and clear documentation of findings, insights, and model behaviors.
Collaborate across disciplines—including research, engineering, product, and design—to shape long-term strategy for search, alignment, and retrieval-augmented systems.
Contribute to a culture of innovation by sharing learnings, mentoring peers, and participating in internal research discussions, reviews, and technical deep dives.