Applied Scientist: Microsoft AI – PhD – Redmond

Come build community, explore your passions and do your best work at Microsoft. This opportunity will allow you to bring your aspirations, talent, potential - and excitement for the journey ahead.

Monetization at Microsoft AI is at the forefront of one of the fastest growing areas on the Internet—online advertising and intelligent monetization solutions. Our work powers products like Bing Ads, Copilot, and the broader Microsoft ecosystem, serving billions of ad impressions and generating terabytes of user interaction data every day. The rapid evolution of this space presents incredible opportunities and complex technical challenges that require cutting-edge solutions in machine learning, natural language processing, data mining, and large-scale optimization.

We are a world-class organization of passionate scientists and engineers working at the intersection of AI and monetization. Our mission is to select and deliver optimized, personalized content and ads across Microsoft surfaces to maximize a total utility function that balances revenue, user experience, and advertiser value. Whether it's helping users discover what they need, enabling advertisers to reach their ideal audience, or infusing intelligence into Copilot interactions, we are redefining what’s possible in the future of monetization through AI.

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.

Please note this application is only for roles based in our Redmond, Washington office. For roles in other offices in the United States, please see our . 

• Building and maintaining production machine learning models for ad retrieval, quality prediction and creative generation.
• 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. 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 decision.