Principal Applied Scientist

Microsoft hybrid • Redmondfull_time
​​The MSAI Search Relevance team is at the forefront of delivering world-class search quality across Microsoft’s ecosystem. We are the driving force behind the relevance of results in Copilot Search experiences and serve as the core retrieval layer in the RAG architecture powering Bizchat. Our impact also extends to maintaining high search quality across traditional endpoints like Outlook, Teams, and SharePoint Search. Our team thrives at the intersection of innovation and applied machine learning.


We are looking for a Principal Applied Scientist to help us deliver 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 and agentic search—to solve complex challenges in Copilot-driven enterprise search.

As a Principal Applied Scientist, you’ll be responsible for delivering mission-critical innovations that directly improve Copilot experiences such as:

  • Agentic Search in modern orchestrator architectures
  • 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 role requires a mix of technical depth, strategic execution, and people management to shape the next generation of AI-powered search experiences.

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.

Requirements

  • Agentic Search in modern orchestrator architectures
  • 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