Applied Science: Phd Microsoft AI Internship Opportunities - Redmond

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Applied Sciences

This internship focuses on applying advanced machine learning techniques to solve complex business challenges in areas like search, personalization, NLP, computer vision, and recommendation systems. The role involves developing and scaling models, preparing datasets, building ML pipelines, and collaborating with cross-functional teams to deliver product-integrated solutions, with a focus on influencing future AI experiences.

What you'd actually do

  1. Analyze and improve advanced machine learning algorithms and systems at scale, optimizing performance across large, complex datasets.
  2. Translate product scenarios and user needs into applied ML problems; design and execute experiments to validate, iterate, and optimize solutions.
  3. Develop and scale models for search, ranking, recommendations, retrieval, and language understanding using modern AI techniques (e.g., deep learning, reinforcement learning, probabilistic methods).
  4. Prepare, clean, and curate high-quality datasets—identifying data quality issues, defining inclusion criteria, and enabling robust feature development.
  5. Build and enhance data and ML pipelines (data collection, preparation, modeling), applying statistical methods to validate assumptions and evaluate model performance.

Skills

Required

  • 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 have

  • 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.

What the JD emphasized

  • PhD
  • internship
  • applied ML problems
  • experiments
  • model performance
  • real-world, product-integrated solutions

Other signals

  • applying 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
  • translate complex business challenges—spanning search, personalization, natural language processing, computer vision, and recommendation systems—into practical, impactful 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)