New 
Applied Scientist II
 Microsoft | |
 United States, Washington, Redmond | |
 Jul 16, 2025 | |
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OverviewAs an Applied Scientist II, 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. This role is available in Redmond, WA or Mountain View, CA. Online Advertising is one of the fastest growing businesses on the Internet today - serving hundreds of millions of ad impressions per day and generating terabytes of user events data every day. The rapid growth of online advertising has created enormous opportunities as well as technical challenges that demand computational intelligence. The Bing Ads Understanding team is at the center stage of this exciting new interdisciplinary field that involves natural language processing, machine learning, data mining, and statistics, to solve challenging problems that arise in online advertising. The central problem of computational advertising is to select an optimized slate of relevant ads for a user to maximize a total utility function that captures the expected revenue, user experience and return on investment for advertisers.We are a world-class R&D team of passionate and talented scientists and engineers who aspire to solve tough problems and turn innovative ideas into high-quality products and services. We help hundreds of millions of users find what they want, and advertisers gain the right audience, thereby directly impacting our business as a Marketplace. We are looking for an Applied Scientist II for our team. 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.Crafting and Optimizing Prompts for Effective Large Language Models (LLMs) 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. | |