2027 Internship - Applied AI Engineering
For over four decades, Cirrus Logic has been propelled by the top engineers in mixed-signal processing. Our rockstar team thrives on solving complex challenges with innovative end-user solutions for the world's top consumer brands. Cirrus Logic is also known for its award-winning culture, which was built on a foundation of inclusion and fairness, meaningful community engagement, and delivering enjoyable employee experiences at every turn. But we couldn’t do it without our extraordinary workforce – and that’s where you come in. Join our team and help us continue to make Cirrus Logic an exceptional place to grow your career!
Cirrus Logic is seeking a student intern to explore how AI and cloud-native engineering can support chip development. This is an R&D-facing opportunity for a master’s or PhD candidate with a strong foundation in computer science, software, or cloud engineering and an interest in working with electrical and hardware design teams. During the internship, you’ll collaborate with engineers to understand selected design and development workflows, investigate where AI may be useful, and build or evaluate a focused prototype. The work may involve Linux-based environments, scripting, cloud-native solutions, and EDA/CAD tools. You’ll receive guidance from experienced engineers and present your approach, findings, and recommendations to the team.
This role does not offer visa sponsorship.
Responsibilities Learn a chip-development workflow from your mentor and R&D partners. Identify opportunities to apply AI to engineering tasks. Build or evaluate a focused AI, scripting, or cloud-native prototype. Use Linux and core software development practices. Explore integration with EDA/CAD or engineering tools. Refine your work with cross-disciplinary team feedback. Document and present your findings and recommendations. Required Skills and Qualifications Enrolled in a bachelor's master’s or PhD program in computer science, computer engineering, electrical engineering, or related field. Programming or scripting experience, such as Python or shell. Familiarity with Linux, version control, testing, or documentation. Coursework or project experience in AI/ML, cloud, DevOps, or software systems. Interest in hardware development and cross-disciplinary collaboration. Clear communication and willingness to learn from feedback. Preferred Skills and Qualifications AI/ML research or projects, especially engineering workflows. Exposure to cloud platforms, containers, CI/CD, or infrastructure automation. EDA/CAD, semiconductor, chip design , or hardware experience. Prototype evaluation, including output quality and limitations. Relevant portfolio, research, publication, or class project.