Applied Science: Microsoft AI Internship Opportunities - Redmond

Analyze and improve advanced machine learning algorithms and systems at scale, optimizing performance across large, complex datasets. Translate product scenarios and user needs into applied ML problems; design and execute experiments to validate, iterate, and optimize solutions. Develop and scale models for search, ranking, recommendations, retrieval, and language understanding using modern AI techniques (e.g., deep learning, reinforcement learning, probabilistic methods). Prepare, clean, and curate high-quality datasets—identifying data quality issues, defining inclusion criteria, and enabling robust feature development. Build and enhance data and ML pipelines (data collection, preparation, modeling), applying statistical methods to validate assumptions and evaluate model performance. Collaborate cross-functionally with scientists, engineers, and product stakeholders to iterate on ideas and deliver real-world, product-integrated solutions. Communicate technical insights and experimental results clearly, while continuously incorporating emerging research, tools, and industry trends to improve solution quality and efficiency. Currently pursuing a Bachelors Degree in Statistics, Econometrics, Computer Science, Artificial Intelligence, Electrical or Computer Engineering, or related field. Must have at least one additional quarter/semester of school remaining following the completion of the internship. Candidate must be enrolled in a full time PhD program in area relevant for the role during the academic term immediately before their internship. Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field Equivalent experience. Explore product challenges using state of the art solutions. Research publications, coursework, or project experience relevant to search, language models, recommender systems, geospatial or location intelligence, or content and commerce systems. Experience running controlled experiments and interpreting offline and online evaluation metrics. Familiarity with large-scale distributed systems or productionizing applied science solutions. Passion for building AI experiences that improve relevance, discovery, personalization, and end-user satisfaction.