Internship - Software Engineering(Cellular)

Summary

Posted: Weekly Hours: 40 Role Number:200579065
Join the Cellular Software and Connectivity Technologies team and be part of an outstanding team of engineers, working with multi-functional teams to drive innovation and develop products used by millions worldwide. We are seeking a highly reciprocal intern who can excel in a fast-paced environment. The people who work here have reinvented and redefined entire industries with our products and services. The same real passion for innovation also applies to our business practices - strengthening our dedication to leave the world better than we found it. You should join Apple if you want to help deliver the next amazing Apple product.

Description

Apple’s Cellular Software team is seeking dedicated, highly motivated interns to work across layers on groundbreaking cellular technologies. The position involves identifying and/or developing core cellular technologies that tackle everyday problems for customers using iPhone, iPad, Watch, and other wireless product lines. We are passionate about keeping our users connected 24/7 wherever they go. This team takes pride in not just coming up with proven software designs but also in redefining traditional models of the cellular ecosystem. We go above and beyond to bring user experience to the next level. Our members enjoy the flexibility to work on all aspects of Cellular Protocol software - Data Path, SIM, NAS, AS, and iOS interfaces, to come up with the best possible solution for any given problem. As part of this team, you will impact iPhone user experience worldwide.

Minimum Qualifications

• Currently enrolled in a Master’s degree or higher (preferably in Computer Science or Electrical Engineering or equivalent)
• C Programming
• Strong debugging skills
• Expertise in Embedded systems

Preferred Qualifications

• Knowledge of 3GPP standards
• Insights into End-to-End Cellular Datapaths for next-generation wireless technologies like LTE / 5G-NR
• Good understanding of Machine-learning algorithms