RF Datapath Correction Algorithms - 
Engineering Intern/Co-op (M/F/D)

At Apple, we work every single day to craft products that enrich people’s lives. The people here at Apple don’t just build products — they create the kind of wonder that revolutionizes entire industries. We invite you to join our dynamic group for the outstanding and exciting opportunity to contribute to upcoming products that will delight and inspire millions of Apple’s customers every day. Are you a student passionate about the intersection of digital signal processing, algorithms, artificial intelligence and wireless technology? Apple's RF Receiver Systems Engineering team in Munich is looking for an enthusiastic and curious student to explore technologies for digital correction of analog/RF impairments and for enhancement of signal quality as required for upcoming 6G standard. This is a unique opportunity to learn from world-class experts, apply your academic knowledge to cutting-edge, real-world challenges, and contribute to the core technology that powers Apple's wireless products. Join our team of leading experts and help us explore how AI can redefine the performance and efficiency of the RF systems that connect our customers to the world. In this role, you will be an integral part of our RF Receiver Systems team, focusing on the research and application of algorithms for signal impairment compensation Your work will include the following tasks: Analyze state-of-art technology and propose new innovative solutions for RF signal path challenges such as IQ mismatch compensation, online calibration, digital post distortion and other correction techniques etc. Research, design, and prototype machine learning models—including neural networks and reinforcement learning Develop and maintain simulation environments in Python or MATLAB to model RF systems and test algorithms Analyze simulation and measurement data to evaluate model performance, identify trade-offs, and propose improvements. Collaborate with senior engineers to understand system requirements and translate them into model objectives and constraints. Document your findings, create presentations, and share your results and insights with the broader team.