2027 Internship - Applied AI Engineering

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 foundationin computer science, software, or cloud engineering and an interest in working with electrical and hardware design teams.

During the internship, you’llcollaborate 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’llreceive 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.
• Identifyopportunities 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 relatedfield.
• 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.
• Interestin hardware development and cross-disciplinary collaboration.
• Clear communication and willingness to learn from feedback.

Preferred Skills and Qualifications

• AI/ML research or projects, especiallyengineering workflows.
• Exposure tocloud 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.