Opportunities · Internship
Software Engineering Intern
Microsoft · Illinois
Role overview
What you'll own
- Develop, test, debug, and maintain software components for AI-powered products, services, or internal tools.
- Contribute production-quality code that is maintainable, secure, performant, and aligned with team standards.
- Contribute to AI features such as agents, tool/API integrations, prompt flows, retrieval workflows, evaluation harnesses, and monitoring capabilities.
- Collaborate effectively with team members in a dynamic, ambiguous environment; adapt quickly across technologies, stacks, and engineering tasks; proactively seek and incorporate feedback; and contribute to shared goals while building technical depth.
Requirements and eligibility
- Currently pursuing an M.
- Ability to communicate clearly, collaborate effectively, adapt across technologies and engineering tasks, and learn quickly in a team environment.
- Currently pursuing a Doctorate in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field, with at least one semester/term remaining after the internship.
- 1+ year of experience developing and applying data structures and algorithms.
Dates for this one
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Posted
Aug 7, 2026
Applications close
Feb 3, 2027
147 days left
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Develop, test, debug, and maintain software components for AI-powered products, services, or internal tools. Contribute production-quality code that is maintainable, secure, performant, and aligned with team standards.
Partner with mentors, engineers, product managers, and stakeholders to understand user requirements and translate them into engineering tasks. Contribute to AI features such as agents, tool/API integrations, prompt flows, retrieval workflows, evaluation harnesses, and monitoring capabilities.
Help define and run tests, evaluations, and quality checks for AI behavior, including accuracy, relevance, groundedness, and reliability. Investigate issues using logs, traces, test results, and debugging tools, and incorporate feedback into improved solutions.
Learn and apply engineering best practices across code quality, security, responsible AI, CI/CD, documentation, and observability. Document work clearly and share implementation details with team members to support continuity and knowledge sharing.
Collaborate effectively with team members in a dynamic, ambiguous environment; adapt quickly across technologies, stacks, and engineering tasks; proactively seek and incorporate feedback; and contribute to shared goals while building technical depth.
Currently pursuing an M.Sc. or M.A. degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field, with at least 3 semesters remaining Programming experience in one or more general-purpose languages such as Python, C#, Java, JavaScript/TypeScript, C++, or similar.
Understanding of core computer science fundamentals, including data structures, algorithms, software design, and debugging. Ability to communicate clearly, collaborate effectively, adapt across technologies and engineering tasks, and learn quickly in a team environment.
Currently pursuing a Doctorate in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field, with at least one semester/term remaining after the internship.
1+ year of programming experience. 6+ months of experience delivering projects in teams, through coursework, internships, open-source work, research, or personal projects.
1+ year of experience developing and applying data structures and algorithms. Familiarity with AI, machine learning, generative AI, LLMs, agents, retrieval-augmented generation, prompt engineering, or evaluation methods.
Basic familiarity with cloud platforms, REST APIs, SDKs, CI/CD, telemetry, or observability. Interest in responsible AI, security, reliability, and building software for regulated or sovereign cloud environments.
Demonstrated entrepreneurial mindset, creativity, initiative, ownership, and persistence in identifying meaningful problems, exploring unconventional solutions, and turning ambiguous ideas into tangible outcomes in “0 to 1” product spaces.
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