Senior Applied Scientist

Microsoft Microsoft · Big Tech · London, United Kingdom +1 · Applied Sciences

Senior Applied Scientist focused on retrieval technologies for Microsoft 365 Copilot, specifically Search, Chat, and Agent experiences. The role involves designing, experimenting with, and evaluating retrieval and ranking systems, including semantic, dense, sparse, and hybrid retrieval, RAG, and LLM-integrated architectures, to enhance grounding, relevance, and reasoning for millions of enterprise users.

What you'd actually do

  1. Design and run experiments, define offline and online evaluation metrics, and develop scalable retrieval pipelines and models for enterprise-scale search systems.
  2. 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.
  3. 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.
  4. Deeply understand user retrieval pain points and enterprise grounding challenges, and develop solutions that materially improve relevance, answer quality, freshness and personalization.
  5. Provide technical leadership and mentorship to scientists and engineers working on retrieval, ranking and recommendation systems.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience OR equivalent experience.
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • Strong 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

What the JD emphasized

  • production scale
  • enterprise-scale search systems
  • production-grade enterprise systems
  • scalable and reliable retrieval infrastructure
  • large-scale production systems
  • enterprise grounding challenges
  • enterprise search

Other signals

  • powers the intelligence behind M365 Copilot
  • building state-of-the-art retrieval systems
  • improve grounding quality, relevance, personalization and reasoning capabilities
  • deliver AI-powered experiences