Cellular RF Transmitter Systems Engineer

Job description

At Apple, we work every single day to craft products that enrich people’s lives. The people here at Apple don’t just create products — they create the kind of wonder that revolutionizes entire industries. We invite you to join our dynamic group, for the unique and rewarding opportunity to contribute to upcoming products that will delight and inspire millions of Apple’s customers every day. Are you interested in doing work that challenges you, expands your thinking, provides innovative solutions for future problems while driving your growth as recognized technical leader in the cellular industry? Apple's RF Transmitter Systems Engineering team is looking for a passionate, talented individual who is eager to break technical boundaries and to change the way of doing things. Our elite team is shaping the mobile market with disruptive RF innovations while taking full responsibility for execution and commitments to schedule and quality. Join our team of leading experts in cellular RF industry and deliver differentiating transmit architectures and solutions for Apple’s wireless products that create amazing value for our customers! Minimum Qualifications: Minimum requirement of a bachelors degree Strong analytical skills and the ability to collaborate and communicate across global teams. Knowledge of cellular radio/3GPP standards (GSM, UMTS, LTE, LTE-A, 5G NR), including RF aspects as well as system use cases and TX waveforms. Familiarity with RF systems and transceiver architecture, including lineup design and component-level trade-offs (e.g. gain, linearity, noise, power, thermal). Preferred Qualifications: Ph.D. in Electrical Engineering or equivalent Initial expertise in transmitter system-level concepts and control procedures like power control, digital predistortion, envelope tracking. Background in digital signal processing. Understanding of SoC integration constraints and multi-chip RF architectures. Solid theoretical grounding and hands-on experience with classical machine learning techniques (e.g., clustering, dimensionality reduction) applied to regression and classification problems. Solid theoretical understanding and hands-on experience with neural networks for regression and classification problems. Practical experience across supervised, unsupervised, and reinforcement learning.