Applied Science: PhD Microsoft AI Internship Opportunities - Redmond

Microsoft•Redmond, WA

1d•$6,810 - $14,610•Onsite

About The Position

Bring your passion for innovation and research to a team at the forefront of artificial intelligence and machine learning. If you’re eager to make significant contributions across user engagement, intelligent experiences, and real product scenarios, this opportunity within Microsoft AI Content and Commerce, Search Fundamentals, and Search Place will excite you. As an Applied Scientist PhD Intern, you will apply your expertise in areas such as supervised and unsupervised learning, deep learning (especially transformers and sequence modeling), reinforcement learning for optimizing user outcomes, and advanced data science techniques. You’ll translate complex business challenges—spanning search, personalization, natural language processing, computer vision, and recommendation systems—into practical, impactful solutions using Azure and other cloud-based technologies. Your experience with statistical analysis, hypothesis testing, large datasets, and deploying robust data pipelines will be valued as you drive research into production. This role allows you to shape the future of Copilot and intelligent content, influencing Microsoft's direction as you directly support our overarching mission and vision. At Microsoft, our mission to empower every person and every organization on the planet to achieve more guides how we partner with customers to deliver trusted, impactful solutions. With a growth‑mindset culture, we innovate responsibly and measure success by shared progress, people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone. Please note this application is only for internships based in our Redmond, Washington office. For internships in other offices in the United States, please see our Careers site.

Requirements

Currently pursuing a Doctorate 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.

Nice To Haves

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)
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.

Responsibilities

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.