CAD Virtuoso Engineering Intern
Summary
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, amazing people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. Join us to help deliver the next groundbreaking Apple product.
Description
As part of our CAD Team, you will:
- Develop AI-based tools that help engineers find information and solve problems more quickly.
- Connect AI models to the engineering systems and data the team uses day to day.
- Assess the quality and reliability of these tools, and look for ways to improve them.
- Automate repetitive engineering and support tasks. - Document your work and present your progress to the team.
Minimum Qualifications
- Studying towards a BSc or MSc in Electrical/Electronic Engineering, Computer Science, Computer Engineering or a related subject.
- Strong programming skills in Python.
- Familiarity with LLMs and their APIs, prompt design, embeddings or vector search, whether through coursework, projects or personal work.
- Comfortable working in a Linux environment, including the shell, Git and scripting.
- Clear written and spoken communication in English.
- Curiosity, and the ability to break an open-ended problem down into measurable steps.
- Fluent English language skills (written and verbal)
Preferred Qualifications
- Exposure to IC design flows such as schematic capture, layout, DRC/LVS or simulation, or to EDA tools such as Cadence Virtuoso.
- Experience with SKILL, Tcl or another domain-specific scripting language.
- Experience with agent frameworks, tool-calling or the Model Context Protocol (MCP).
- Understanding of how to evaluate machine-learning systems, such as test-set construction, precision/recall and human-in-the-loop review.
- Experience building small web front ends or dashboards.