Machine Learning Engineer, Cellular RF Transmit

A systems engineering role in the RF transmitter team of a global consumer electronics company, defining the transmit architectures of its wireless products and bringing machine learning into how they are controlled and calibrated.

The role

• Translating system-level and performance requirements into requirements for the transmit line-up and transmit features, including RF algorithm specifications for RFICs and system firmware.
• Specifying RF algorithms such as power control and calibration, and defining the RF control infrastructure: compute resources, interfaces, memory and timing.
• Researching, designing and prototyping machine learning models, including neural networks and reinforcement learning, for power control, calibration and digital pre-distortion.
• Building and maintaining simulation environments in Python or MATLAB to model RF systems and test the algorithms, and analysing the results.

What is needed

• Proven experience in RF including cellular transmit architectures, or a PhD with previous work on cellular topics.
• Knowledge of 3GPP standards (GSM, UMTS, LTE, LTE-A, 5G NR), including their RF aspects and transmit waveforms.
• Familiarity with transceiver architecture, line-up design and component-level trade-offs.
• Hands-on machine learning: classical techniques, neural networks, and supervised, unsupervised and reinforcement learning.
• A BSEE or MSEE, and proficient English.

Experience of power control, digital pre-distortion or envelope tracking, a DSP background, SoC and multi-chip RF architectures, and a PhD in electrical engineering are all preferred.