Enabling Edge Intelligence (E2I) Intern - Summer 2027

Carnegie Mellon University•Pittsburgh, PA
•Onsite

About The Position

The Enabling Edge Intelligence (E2I) Intern will contribute to the E2I team’s work on testing AI capabilities in edge environments. The team develops tools and practices for integrating AI into software systems, emphasizing production readiness and resource optimization. This focus area seeks more efficient and effective ways to test qualities of edge AI systems, including security, safety, and resource efficiency. The intern will investigate approaches for testing AI capabilities, explore how generative AI and agentic tools can support that testing, and integrate testing approaches into an existing ML Test and Evaluation (MLTE) process and toolset. The intern will also help share results through peer-reviewed articles, blog posts, and other relevant publications. The role is based onsite at the SEI Pittsburgh office and is available during the summer.

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; 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 internally and externally with professional engineers and researchers.
  • Be willing to learn new technologies.

Nice To Haves

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

Responsibilities

  • Investigate new and effective approaches for testing AI capabilities.
  • Create ways to leverage 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.
  • Contribute to sharing results through peer-reviewed articles, blog posts, and other relevant publications.
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