Enabling Edge Intelligence (E2I) Intern - Summer 2027

Carnegie Mellon University•Pittsburgh, PA

2d•Onsite

About The Position

The SEI’s Enabling Edge Intelligence (E2I) team includes software engineers, researchers, and data scientists who develop tools and practices for integrating AI capabilities into software systems operating in edge environments, with an emphasis on production readiness and resource optimization. A focus area is improving the efficiency and effectiveness of testing key qualities of edge AI systems, including security, safety, and resource efficiency. The intern will contribute to activities in this focus area, including research and development and documentation of results. The position is based onsite at the SEI Pittsburgh office and is for summer 2027. The Software Engineering Institute (SEI) advances software engineering principles and practices and serves as a national resource in software engineering. It works with defense and government organizations, industry, and academia to help organizations improve software engineering capabilities and develop or acquire software that is defect-free, within budget, and on time.

Requirements

Be enrolled in an undergraduate or graduate program in software engineering, computer science, or a related field.
Be a U.S. citizen and eligible to work in the United States without visa sponsorship; the role is subject to a background check.
Be able to work an internship during the summer and onsite at the SEI Pittsburgh office.
Have the ability to think critically and solve problems.
Have a solid understanding of software engineering and software testing.
Be proficient in Python.
Be proficient in using generative AI and agents for code development.
Be able to collaborate with professional engineers and researchers internally and externally.
Be willing to learn new technologies.

Nice To Haves

Experience developing agentic systems.
Experience with test and evaluation tools and processes.
Experience documenting and communicating findings clearly.

Responsibilities

Investigate new and effective approaches for testing AI capabilities.
Develop ways to use generative AI and agentic tools and technologies for testing AI capabilities.
Integrate testing approaches into an existing ML Test and Evaluation (MLTE) process and toolset.
Share results through peer-reviewed articles, blog posts, and other relevant publications.
Allocate work across research and development (80%) and documentation of results (20%).