Machine Learning Engineer

Silvus Technologies (Motorola Solutions) is seeking a Machine Learning Engineer to develop ML-driven features that improve advanced MIMO radios and wireless networking systems. The role focuses on designing and implementing machine learning algorithms using real-world RF datasets, then integrating predictive models into prototypes for challenging RF environments.

• Research, design, and implement machine learning algorithms for wireless communication systems (link adaptation, interference mitigation, anomaly detection, spectrum sensing)
• Analyze real-world radio frequency datasets to develop predictive models
• Develop software prototypes and integrate ML algorithms with wireless networking systems
• Collaborate with experts in wireless communications, DSP, networking, and embedded systems
• Apply ML-driven techniques to improve performance and adaptability in dynamic RF environments

The technical scope is ML for wireless communications: link adaptation, interference mitigation, anomaly detection, and spectrum sensing based on radio frequency datasets. The engineer will collaborate with wireless communications, DSP, networking, and embedded systems experts to integrate ML algorithms into software prototypes and embedded/networking components for MIMO radios.

The ideal candidate is a mid-level Machine Learning Engineer who has implemented machine learning algorithms for wireless communication systems using real-world radio frequency (RF) datasets. They are strong in applying ML techniques like link adaptation, interference mitigation, anomaly detection, and spectrum sensing, and can collaborate with experts in DSP, networking, and embedded systems to integrate ML-driven features into MIMO radios. The candidate is comfortable working in a hybrid role with at least three onsite days per week in West Los Angeles.