Internship - Software Engineering - AI-Augmented Static Code Analysis

Static analysis helps engineers find software defects early — and there's a real opportunity to push it further with modern AI. As a Software Engineering Intern with the Wireless Technologies & Ecosystems team, you'll explore how the latest large language models can be combined with established static analysis techniques to improve how issues in code are detected.

It's an open, hands-on problem at the intersection of program analysis, software security, and applied AI — with plenty of room to experiment and make a visible contribution. If you enjoy reasoning about code and applying AI to practical engineering problems, we'd like to meet you.

The Wireless Technologies & Ecosystems team builds tools and infrastructure that help engineers develop high-quality software. In this internship, you'll investigate how AI — in particular large language models (LLMs) — can enhance static code analysis, working alongside experienced engineers in static analysis and applied AI.

You'll help prototype approaches, evaluate how well they work, and build tooling to measure and compare results. Because this is an open problem with no established playbook, you'll have the freedom to experiment with different techniques and see your ideas tested against real engineering questions.

Minimum Qualifications

Currently enrolled in a Bachelor's or Master's program in Computer Science, Computer Engineering, or a related field.
Hands-on programming experience in C and/or C++ — you have actively written, debugged, or analyzed C/C++ code (e.g. in a university project or thesis) and are comfortable with the fundamentals: pointers, memory allocation, and structs. (You do not need to be a C++ expert.)
Proficiency in at least one scripting language, Python strongly preferred.
Strong analytical and problem-solving skills, and the ability to reason clearly about unfamiliar code — we care more about how you think through a problem than about a memorized answer.
Good written and spoken English.

Preferred Qualifications

Coursework, projects, or interest in static code analysis, program analysis, or software security.
Familiarity with large language models (LLMs) and generative-AI tooling, and an intuition for where AI-based approaches help and where they don't.
Familiarity with modern AI developer tools (e.g. LLM-assisted coding assistants).
Exposure to open-source software development tools.
A collaborative, curious, and self-driven attitude — comfortable working on an open problem where the right answer is not known in advance.