Senior Applied Scientist-Ads Monetization

Microsoft•Redmond, WA

4d•$119,800 - $261,000•Hybrid

About The Position

Microsoft Monetization is building the next generation of AI-powered advertising and commerce experiences across Search, Audience, 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 advertising systems across Microsoft Ads, Bing, Copilot, and beyond. You will work on large-scale machine learning challenges spanning user understanding, ad representation, retrieval, recommendation, ranking, and optimization, with the goal of maximizing value for both users and advertisers. You will apply and advance technologies in natural language processing (NLP), computer vision (CV), large language models (LLMs), Generative AI, and Agentic AI. You will have a direct impact on millions of users and advertisers by delivering robust and scalable solutions that improve advertising experiences and business outcomes. This role is part of the Microsoft AI Experiences - Monetization team and is responsible for solving end-to-end machine learning and optimization problems across 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.

Requirements

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)
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)
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)
Equivalent experience.

Nice To Haves

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)
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
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).
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 a team to deliver innovative solutions solving challenging business/technical problems from high level vision and architecture, down to quality design and implementation.
Experience evaluating SLMs, LLMs, or agentic systems using task-specific offline metrics, human or model-assisted evaluation, safety and robustness testing, latency and cost analysis, and controlled online experiments.
Experience designing and implementing agentic AI systems that use tool calling, retrieval, planning, memory, structured outputs, multi-step workflows, or multi-agent coordination, with appropriate safeguards and observability.
Experience applying responsible AI practices to model and agent development, including evaluation for safety, reliability, privacy, security, bias, groundedness, and misuse risks.
Have publications at peer-reviewed Data Science/AI conferences (e.g. KDD- Knowledge Discovery and Data Mining, CIKM- Conference on Information and Knowledge Management, SIGIR- Special Interest Group on Information Retrieval, NeurIPS- Neural Information Processing Systems, CVPR- Computer Vision and Pattern Recognition, ICML International Conference on Machine Learning, ICLR- International Conference on Learning Representations, ICCV- International Conference on Computer Vision, and ACL- Association for Computational Linguistics).

Responsibilities

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.