Senior Applied Scientist-Ads Relevance

Microsoft Monetization is building the next generation of AI-powered advertising and commerce experiences across Search, Shopping, Copilot, and emerging agentic surfaces. As conversational agents increasingly become the interface between users and businesses, we are reimagining how products, services, and ads are discovered, selected, personalized, and delivered. In this role, you will design and implement cutting-edge machine learning models and algorithms that power relevance systems across Microsoft Ads, Bing users, Copilot, and beyond.  You will have a direct impact on millions of users and advertisers, delivering scalable solutions to enhance ad relevance and optimize user experiences.   This role is part of Microsoft Artificial Intelligence (MAI)-Ads Engineering and is responsible for the end-to-end relevance problem for our ads products.  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.   Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Defining the ad relevance problem across different ad scenarios to optimize both the user and advertiser experience.  Driving algorithmic and modeling improvements to the system using primarily deep learning techniques from NLP and computer vision, including the latest LLM models.  Deploying robust and scalable solutions to continuously improve ad relevance.  Analyzing model and system performance to identify opportunities based on offline and online testing.

Required Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 4+ years of experience developing natural language processing or multimodal machine learning systems using deep learning, including hands-on experience with transformer-based small language models (SLMs) or large language models (LLMs).  OR 4+ years working experience in Computer Vision (CV) with latest deep learning technologies including Vision Transformers.  4+ years of experience developing and operating production machine learning or AI systems using Python, C++, or equivalent programming languages.  Experience in online advertising.  Experience with distributed training or inference for SLMs and LLMs, including data and model parallelism, mixed-precision training, checkpointing, experiment management, performance optimization, and efficient serving.  Ability to work independently in