# Senior Applied Scientist **Company**: Microsoft **Location**: London **Experience**: senior **Job type**: full-time **Salary**: £ 74,700.00 – £ 122,600.00 per year **Category**: IT **Industry**: Technology **Ticker**: MSFT **Wikidata**: https://www.wikidata.org/wiki/Q2283 **Apply**: https://microsoft.ai/job/senior-applied-scientist-89/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_92d0a4f6-fa7 ## Description We are looking for a Senior Applied Scientist with expertise in modern retrieval technologies to help shape the future of Microsoft 365 Copilot. This role sits within the Copilot and Agents Core (CACore) organization, which powers the intelligence behind Microsoft 365 Copilot by combining advances in generative AI with personalized search, retrieval, ranking and recommendation systems. **Responsibilities** - Build state-of-the-art retrieval systems that serve millions of enterprise users every day. - Research, design and evaluate retrieval and ranking technologies. - Improve grounding quality, relevance, personalization and reasoning across Microsoft 365 Copilot experiences. - Influence technical strategy and help shape the future retrieval architecture for Copilot. - Translate scientific advances into reliable, high-impact product capabilities. **Collaboration and Impact** You will work in an exciting, collaborative environment and partner closely with engineering, product and platform teams. You will also collaborate across Microsoft Research, Azure AI and product groups to deliver AI-powered experiences that help people accomplish more with less effort. **Culture and Values** Microsoft’s mission is to empower every person and every organization on the planet to achieve more. Employees bring a growth mindset, innovate to empower others and collaborate to realize shared goals. We build on the values of respect, integrity and accountability to create an inclusive culture in which everyone can thrive. **Areas of Focus** - 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. Areas of focus include: - Semantic retrieval using late-interaction architectures such as ColBERT - Dense retrieval and embedding model fine tuning - Modern lexical retrieval approaches such as SPLADE - Hybrid retrieval systems combining dense + sparse retrieval - Query understanding and representation learning - Multi-stage ranking and retrieval optimisation - Retrieval-augmented generation (RAG) - Personalization and contextual ranking - Knowledge retrieval for agentic AI systems - Reinforcement learning and reasoning-aware retrieval systems - LLM-integrated retrieval architectures - Apply best practices in Responsible AI, Privacy-Preserving ML, and scalability for production-grade enterprise 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 Candidates must meet one of the following requirements: - Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical Engineering, Computer Engineering, or a related field and 4+ years of related experience in statistics, predictive analytics, research, or a related discipline; - Master’s Degree in Statistics, Econometrics, Computer Science, Electrical Engineering, Computer Engineering, or a related field and 3+ years of related experience in statistics, predictive analytics, research, or a related discipline; - Doctorate (PhD) in Statistics, Econometrics, Computer Science, Electrical Engineering, Computer Engineering, or a related field and 1+ year of related experience in statistics, predictive analytics, research, or a related discipline; - Equivalent practical experience. ### Preferred Qualifications The ideal candidate will have hands-on experience designing, developing, and deploying retrieval and ranking systems at production scale, with demonstrated expertise in one or more of the following areas: #### Retrieval and Ranking Systems - Semantic retrieval - Dense retrieval systems - Embedding model training and fine-tuning - SPLADE and sparse retrieval methodologies - Hybrid retrieval architectures - Search and recommendation ranking systems - Large-scale information retrieval platforms #### Machine Learning & AI - Strong proficiency in Python and modern machine learning frameworks, such as PyTorch - Experience developing and deploying machine learning systems in production environments - Experience building retrieval systems for Retrieval-Augmented Generation (RAG) and agentic AI architectures - Experience integrating retrieval systems with LLM-based products - Knowledge of reinforcement learning and retrieval-aware reasoning systems #### Evaluation & Experimentation - Experience evaluating retrieval quality through offline metrics and online experimentation - Ability to define and measure ranking effectiveness, relevance, and end-user impact #### Scalability & Infrastructure - Experience optimizing retrieval latency, scalability, and serving infrastructure - Familiarity with enterprise search, personalization, and recommendation systems #### Research Excellence - Track record of research contributions and publications in top-tier venues, including: - SIGIR - RecSys - KDD - WWW - WSDM - ACL - EMNLP Candidates with a demonstrated ability to bridge cutting-edge retrieval research with large-scale, production-ready AI systems will be particularly well aligned to the role. ### Additional Requirements - Ability to meet Microsoft, customer, and/or government security screening requirements. - Must successfully pass the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter. Applied Sciences IC4 – The typical base pay range for this role across United Kingdom is £ 74,700.00 – £ 122,600.00 per year. Certain roles may be eligible for benefits and other compensation. ## Skills ### Required - Python - modern machine learning frameworks - retrieval and ranking systems - semantic retrieval - dense retrieval systems - embedding model training and fine-tuning - SPLADE and sparse retrieval methodologies - hybrid retrieval architectures - search and recommendation ranking systems - large-scale information retrieval platforms - reinforcement learning - retrieval-aware reasoning systems ### Nice to have - Retrieval-Augmented Generation (RAG) - agentic AI architectures - LLM-based products - evaluation and experimentation - scalability and infrastructure - research excellence --- Source: [Apply at microsoft.ai](https://microsoft.ai/job/senior-applied-scientist-89/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)