New 
Senior Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 May 20, 2025 | |
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OverviewMicrosoft Audience Network (MSAN) - Signals team part of the Microsoft Artificial Intelligence (MAI) is seeking a Senior Applied Scientist to join us in Mountain View, CA or Redmond, WA.This is an exceptional opportunity to apply your advanced skills in one of the fastest-growing businesses, to solve real-world problems on a scale. Our goal at MSAN is to maximize value for users and advertisers through our platform.As a Senior Applied Scientist, you will specialize in creating and enhancing machine learning technologies in areas such as natural language processing (NLP), computer vision (CV), and large language models (LLM). You will be a key player within a dynamic team, contributing to and collaborating with other talented colleagues on cutting-edge machine learning challenges from a real ads recommender system.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.
ResponsibilitiesBuilding and maintaining production machine learning models for ad retrieval, quality prediction and ad ranking. Finding insights and forming hypothesis on web-scale data with various machine learning, feature engineering, statistical, and data mining techniques: e.g. regression, classification, NLP, optimization, p-values analysis. Designing experiments, understanding the resulting data, and producing actionable, trustworthy conclusions from them. Craft and Optimize Prompts for Effective LLM Performance: Design, test, and refine prompts to elicit accurate, relevant, and useful responses from LLMs. This involves understanding the nuances of how the model interprets different inputs, experimenting with various prompt formulations, and iterating based on performance metrics and user feedback. Wrangling large amounts of data (think petabytes) using various tools, including open-source ones and your own. Taking complex problems and the associated data and giving the answers in a concise form to assist senior executives in making key business decisions. | |