Applied Scientist II

Microsoft•Redmond, WA

3d•$100,600 - $199,000

About The Position

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, computer vision, 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. 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 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.
Experience with Large Language Models: Demonstrated experience working with LLMs/VLMs, such as GPT, BERT, or similar models, including knowledge of their strengths, limitations, and capabilities.
Solid Understanding of NLP and CV: In-depth knowledge of natural language processing (NLP) and computer vision techniques and concepts, including tokenization, semantic analysis, and text generation, multimodal representation, etc.

Nice To Haves

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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.
Experience delivering, scaling, and maintaining highly successful and innovative machine learning products with your fingerprints all over them.
Experience in parallel or distributed processing, high performance computing, stream computing and SCOPE.
Understanding of state-of-the-art computer vision, machine learning and deep learning technologies. In particular, hands-on experiences with deep learning models (Transformers, DNN, Attention, CNN, RNN), multimodal modeling, and frameworks (PyTorch, TensorFlow, Keras, etc.) will be very helpful.
Solid algorithm and analytical background and very good understanding on how to apply advanced knowledge to solve real problems
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.

Responsibilities

Building and maintaining production machine learning models to generate image and text assets, multimodal representation and predict ad quality.
Finding insights and forming hypothesis on web-scale data with various machine learning, computer vision, 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 LLM and VLM Performance: Design, test, and refine prompts to elicit accurate, relevant, and useful responses from LLMs/VLMs. 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. All programming languages are welcome, especially Python, R, C#, C++, Java, and SQL.
Taking complex problems and the associated data and giving the answers in a concise form to assist senior executives in making key business decisions.

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