Job Description

Do you want to lead cutting-edge innovation that impacts hundreds of millions of users across the Microsoft ecosystem? The Microsoft Search Relevance team is at the forefront of powering core Copilot experiences, including Copilot Chat, Copilot Search, and BizChat RAG--driving productivity and helping users find the right information within their organizations and across the web.

We are looking for a

Principal Applied Science Manager

to build and lead a high-performing team delivering breakthrough applied machine learning and information retrieval solutions at enterprise scale. This role is a unique opportunity to apply state-of-the-art techniques--including dense retrieval, hybrid search, multilingual large language models (LLMs), RAG (Retrieval-Augmented Generation), and transformer-based re-ranking models--to solve complex challenges in Copilot-driven enterprise search.

As a technical and people leader, you'll be responsible for delivering mission-critical innovations that directly improve Copilot experiences. Your team will work on:Adapting advanced vector search algorithms (e.g., FAISS, ANN, ScaNN) for enterprise-scale semantic retrieval Improving classic and neural keyword search quality through deep language understanding Designing and training relevance models, including LLM fine-tuning and learning-to-rank (LTR) approaches Building robust evaluation pipelines using offline metrics and online A/B experimentation Driving cross-org collaboration with platform partners, other applied science teams, and product teams across time zones

This is a highly impactful role requiring a mix of technical depth, strategic execution, and people management to shape the next generation of AI-powered search experiences.

At Microsoft, we are united by our mission to empower every person and every organization on the planet to achieve more. We foster a culture driven by growth mindset, collaboration, and inclusive innovation, rooted in our core values of respect, integrity, and accountability.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities

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Drive end-to-end applied science projects: From ideation and design to implementation, experimentation, and shipping, you will lead high-impact projects that directly improve Copilot Chat, Copilot Search, and BizChat experiences. This includes identifying search and relevance gaps, formulating innovative hypotheses, and delivering scalable solutions. Lead and grow a high-performing applied science team: Manage, mentor, and empower a team of applied scientists--owning their technical direction, project execution, and career development. You will guide day-to-day work, ensure scientific and engineering rigor, and be accountable for the team's output and impact. Innovate with scientific rigor: Invent and apply cutting-edge techniques in machine learning, natural language processing, and information retrieval to address real-world challenges at enterprise scale. You will design novel approaches for improving retrieval, ranking, query understanding, and semantic search in Copilot systems. Document, share, and amplify learnings: Promote a culture of transparency and innovation by capturing experimental results, documenting methodology, and publishing internal learnings. You'll drive knowledge sharing that enables broader impact across the organization. Translate business goals into scientific strategy: Partner closely with product and business stakeholders to align team efforts with high-priority objectives. You will translate ambiguous product requirements into clear, data-driven, and technically feasible directions. Collaborate across organizations and time zones: Work cross-functionally with platform engineering teams, peer science orgs, and product managers to ensure alignment, resolve dependencies, and unblock progress. You'll be a key bridge between applied science innovation and product delivery.

Qualifications

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Required Qualifications:

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.
3+ years of people management experience. 4+ years of hands-on experience leading applied science projects from ideation to production--preferably in high-scale environments such as search, recommendation, or conversational AI. 8+ years of industrial programming experience in modern languages such as Python, Java, C++, or C#, including production-level ML pipelines.

Preferred Qualifications:

5+ years of direct people management experience, including recruiting, performance management, and career development. 3+ years managing applied science teams, driving innovation in real-world product contexts. Deep experience in search and ranking systems, semantic retrieval, and information retrieval at scale. Applied experience with state-of-the-art retrieval and ranking techniques, including:
+ Dense retrieval models (e.g., DPR, ANCE, ColBERT)
+ Vector search systems (e.g., FAISS, ScaNN, Milvus, Pinecone)
+ RAG (Retrieval-Augmented Generation) architectures using LLMs such as OpenAI GPT, T5, or Llama.
+ Fine-tuning or prompt engineering of large-scale language models for query rewriting, summarization, and document reranking
Familiarity with hybrid search strategies, learning-to-rank (LTR) frameworks, and evaluation methodologies for IR (Information Retrievel) systems (offline metrics, A/B testing, relevance judgments). Demonstrated expertise in data analysis at scale, including working with logs, telemetry, and large datasets to uncover behavioral patterns, build evaluation datasets, and derive insights. Familiarity with modern machine learning and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers.

Applied Sciences M6 - The typical base pay range for this role across the U.S. is USD $163,000 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until October 28, 2025.

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Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.