Software Engineer: Ai/ml & LLM Intern Opportunities for University Students, Redmond

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Software Engineering Intern focused on building AI-powered products and platforms using ML and LLMs. Responsibilities include developing, evaluating, and optimizing AI experiences, potentially contributing to model integration, retrieval systems, inference services, evaluation frameworks, or AI infrastructure. The role emphasizes applying engineering principles to solve complex problems and learning new methods.

What you'd actually do

  1. Applies engineering principles to solve complex problems through sound and creative engineering.
  2. Works with appropriate stakeholders to determine user requirements for a feature.
  3. Quickly learns new engineering methods and incorporates them into work processes.
  4. Seeks feedback and applies internal or industry best practices to improve technical solutions.
  5. Demonstrates skill in time management and completing software projects in a cooperative team environment.

Skills

Required

  • Currently pursuing Bachelor’s Degree in Computer Science or related technical field with at least one semester/term remaining following the completion of the internship
  • OR currently pursuing Master’s Degree in Computer Science or related technical field with at least one semester/term remaining following the completion of the internship.

Nice to have

  • 1+ year(s) of programming experience.
  • 6+ months of experience in delivering projects in teams.
  • 1+ year(s) of experience in developing and applying data structures and algorithms.
  • Coursework, projects, or prior experience in machine learning, artificial intelligence, or data science.
  • Experience programming in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or similar technologies.
  • Understanding of machine learning concepts, model evaluation, retrieval systems, large language models, or prompt engineering.
  • Familiarity with APIs, cloud services, distributed computing, search/ranking, RAG architectures, or performance optimization.

What the JD emphasized

  • AI/ML & LLM
  • machine learning, large language models, and intelligent services
  • AI experiences
  • model integration, retrieval systems, inference services, evaluation frameworks
  • AI applications at scale
  • modern AI technologies
  • machine learning, artificial intelligence, or data science
  • machine learning concepts, model evaluation, retrieval systems, large language models, or prompt engineering
  • RAG architectures

Other signals

  • build AI-powered products and platforms
  • leverage machine learning, large language models, and intelligent services
  • develop, evaluate, and optimize AI experiences
  • model integration, retrieval systems, inference services, evaluation frameworks
  • infrastructure that enables AI applications at scale