Principal Applied Scientist in London

We are looking for a Principal Applied Scientist with deep expertise in modern retrieval technologies to help shape the future of Microsoft 365 Copilot, focusing on Search, Chat and Agent experiences. This role sits within the Copilot and Agents Core (CACore) organization, which powers the intelligence behind M365 Copilot by combining cutting-edge advances in generative AI with personalized search, retrieval and recommendation systems.

As a Principal Applied Scientist in CACore, you will work in an exciting and fast-paced, collaborative environment focused on building state-of-the-art retrieval systems that serve millions of enterprise users daily. You will partner closely with engineering, product and platform teams to innovate, design and evaluate retrieval and ranking technologies that improve grounding quality, relevance, personalization and reasoning capabilities across Microsoft 365 Copilot experiences.

This is a high-impact role where you will influence technical strategy, shape retrieval architecture, and collaborate across Microsoft Research, Azure AI and product groups to deliver AI-powered experiences that help users accomplish more with less effort.

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

• Advance Retrieval Science: Design and run experiments, define offline and online evaluation metrics, and develop scalable retrieval pipelines and models for enterprise-scale search systems.
• Drive Product Innovation: Partner with Engineering, PM and Design to translate product requirements and research advances into scalable and reliable retrieval infrastructure supporting Copilot Search, Chat and Agent experiences.
• Collaborate Across Microsoft: Work closely with Microsoft Research, Azure AI platform teams and product organizations to bring cutting-edge retrieval and ranking advances into large-scale production systems.
• Champion Customer Impact: Deeply understand user retrieval pain points and enterprise grounding challenges, and develop solutions that materially improve relevance, answer quality, freshness and personalization.
• Lead and Mentor: Provide technical leadership and mentorship to scientists and engineers working on retrieval, ranking and recommendation systems. Help establish best practices and contribute to the broader retrieval science strategy across CACore.
• Define Success: Establish and evolve evaluation frameworks and success metrics for retrieval quality, grounding relevance, ranking effectiveness and downstream Copilot quality metrics.
• Stay Ahead: Keep up with the latest advances in retrieval and ranking research, including developments in semantic retrieval, sparse retrieval, RAG systems and LLM-grounded search. Publishing at top-tier venues such as SIGIR, RecSys, WSDM, KDD, ACL and EMNLP is encouraged.

Qualifications

Required Qualifications: Bachelor's, Masters, or Doctorate Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics predictive analytics, research), OR equivalent experience.

Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.

Preferred Qualifications: Solid hands-on experience developing retrieval or ranking systems at production scale. Demonstrated expertise in one or more of the following: Semantic retrieval, Dense retrieval systems, Embedding model training or fine tuning, SPLADE or sparse retrieval methods, Hybrid retrieval architectures, Ranking systems for search or recommendation, Large-scale information retrieval systems, Experience developing ML systems in Python and modern ML frameworks such as PyTorch. Experience evaluating retrieval quality using offline metrics and/or online experimentation. Experience developing retrieval systems for RAG or agentic AI architectures. Publications in top-tier conferences such as SIGIR, RecSys, KDD, WWW, WSDM, ACL or EMNLP. Experience shipping retrieval systems integrated with LLM-based products. Familiarity with enterprise search, personalization and recommendation systems. Experience optimizing retrieval latency, scalability and serving infrastructure. Experience with reinforcement learning or retrieval-aware reasoning systems.