Cellular RF Receiver Algorithms Systems Engineer
Apple
Software Engineering
Waltham, MA, USA
USD 162,500-286,400 / year + Equity
Posted on Apr 12, 2026
Apple’s RF System Engineering team is seeking a motivated and dedicated expert to define the next generation of Cellular RF Receiver architectures. In this role, you will architect and deliver groundbreaking wireless solutions for future Apple products, working on today's 5G New Radio and blazing the path to 6G. You will have a unique opportunity to develop the core technologies that enable an outstanding wireless experience for millions of customers worldwide.
In this role, you will drive the control architecture and algorithms for the RF receiver, collaborating across systems engineering, RF circuit design, digital implementation, platform architecture, software engineering, and production teams to define and deliver solutions that push the boundaries of performance, quality, and implementation efficiency.
- Translate 3GPP, system-level and performance requirements into concepts and specifications for the RX line-up and RX features.
- Contribute to the entire product lifecycle, from initial architectural concept and algorithm definition until silicon validation and mass production.
- Collaborate with RF circuit design teams to understand RF impairments, configuration and dynamic requirements.
- Develop innovative concepts and algorithms to effectively control receiver components, calibrate, and compensate for RF impairments to ensure optimal system performance.
- Conduct conceptual work on novel digital signal processing (DSP) algorithms, including advanced multi-rate digital filter structures, to achieve efficient signal line-up at optimized power consumption and minimal area.
- Run simulations (e.g., MATLAB, Python, C++) for performance analysis.
- Lead the hardware and software partitioning within the transceiver/modem topology, optimizing the architecture for efficiency.
- Author detailed specifications and build reference models for hardware and software implementation teams.
- Collaborate with baseband system engineering teams on system requirements, optimal system partitioning and complex modem use cases.
- BS and 10+ years of relevant industry experience.
- Understanding of Cellular Radio/3GPP Standards (UMTS, LTE, 5G NR FR1/2) and familiarity with emerging 6G research directions.
- Proven knowledge of RF receiver architectures from antenna to analog/digital base band processing including a comprehensive understanding of RF circuit impairments and their impact on system performance.
- Deep understanding of cellular system use cases and RF/baseband interactions and procedures.
- System Engineering experience with a deep background in DSP for wireless communication systems. Experience in refining from abstract definition towards bit true reference models.
- Proficiency in hardware/software co-design and a comprehensive grasp of the trade-offs inherent in digital hardware and software/firmware implementations.
- Expert-level proficiency in Python or C++ is required.
- Experience applying Artificial Intelligence (AI) or Machine Learning (ML) to improve multi-objective optimizations and accelerate engineering workflows.
- Excellent analytical and problem-solving skills, with the ability to collaborate effectively in a global, multi-disciplinary team.
- Expertise in microcontroller architectures used for real time operation and how to program them.
- Proficiency in AI/ML native applications, with experience utilizing tools such as TensorFlow or PyTorch.
- Familiarity with RFIC design, layout considerations, and semiconductor processes.
- Knowledge of cellular modem architectures including their associated concepts and algorithms.
- PhD degree with 3+ years of industry experience in RF, focus on cellular receiver architectures.
- Proficiency in mathematical modeling and analysis, with the ability to build abstract system models leveraging control theory for understanding and designing control and closed-loop feedback systems, optimization theory for multi-objective trade-space analysis and statistical methods for performance modeling under uncertainty.
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