Senior Applied Scientist · Applied Sciences

Overview

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.

What You Will Do

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

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. 

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 

You will 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; OR

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; OR

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; OR

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.

Find additional benefits and pay information here:

https://careers.microsoft.com/v2/global/en/corporate-pay/united-kingdom-corporate-pay.html

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.