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Salary: EUR 63,900 - 120,000 / annual
Apple's RF Transmitter Systems Engineering team is seeking a senior engineer to design and optimize cellular RF transmitter architectures for next-generation wireless products. You will work at the intersection of classical RF systems engineering and modern machine learning, translating system-level performance requirements into specifications for transmitter line-ups, RF algorithms, and control infrastructure.
Key responsibilities include:
- Translating system and performance requirements into TX specifications and RF algorithm requirements for RFICs and firmware
- Specifying RF algorithms (power control, calibration) and defining RF control infrastructure including compute resources, interfaces, memory, and timing constraints
- Researching, designing, and prototyping machine learning models—neural networks and reinforcement learning—for RF applications such as power control, calibration, and digital pre-distortion
- Developing and maintaining simulation environments in Python or MATLAB to model RF systems and validate AI algorithms
- Analyzing simulation data to evaluate model performance, identify trade-offs, and propose improvements
- Collaborating with architecture, systems, RF design, and software teams across global, multicultural organizations
This role offers the opportunity to work on disruptive RF innovations that shape the mobile market while taking full responsibility for execution, schedule, and quality commitments.
REQUIREMENTS:
- Proven years of relevant experience in RF including cellular transmit architectures, OR PhD degree with previous work on cellular topics
- Knowledge of cellular radio/3GPP standards (GSM, UMTS, LTE, LTE-A, 5G NR) including RF aspects, system use cases, and TX waveforms
- Familiarity with RF systems and transceiver architecture, including lineup design and component-level trade-offs (gain, linearity, noise, power, thermal)
- Solid theoretical grounding and hands-on experience with classical machine learning techniques (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
- Strong analytical skills and ability to collaborate across multicultural, global teams
- BSEE or MSEE degree
- Proficiency in English
PREFERRED:
- Initial expertise in transmitter system-level concepts and control procedures (power control, digital predistortion, envelope tracking)
- Background in digital signal processing
- Understanding of SoC integration constraints and multi-chip RF architectures
- PhD in Electrical Engineering or equivalent
