Machine Learning Engineer

Motorola Solutions
Los Angeles, CA$120k – $160kPosted 14 April 2026

Job Description

Company Overview At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future. Department Overview Silvus Technologies, a leading provider of advanced MANET and MIMO communications systems, is reshaping mesh network technology for mission-critical applications – on the ground, in the air and at sea. Its battle-proven StreamCaster family of MANET radios and proprietary MN-MIMO waveform provides the vital communications link for defense, law enforcement and public safety agencies around the world, and in the toughest operational environments. With deep roots in DARPA research, Silvus Technologies develops world-class advanced communications technologies that are reshaping the tactical communications landscape. From pure line-of-sight to extreme non-line-of-sight, Silvus radios form a self-healing, self-forming mesh network, enabling secure and reliable connectivity, including video and high-bandwidth data. Silvus Technologies is a wholly owned subsidiary of Motorola Solutions, Inc. Job Description Would you like to join an incredibly talented group of people, doing very challenging work, with the prime directive of “ Keeping Our Heroes Connected ”? THE OPPORTUNITY Silvus is seeking a Machine Learning Engineer who will report to the R&D Director, Machine Learning on the R&D team.  The successful individual in this role will focus on applying machine learning and data-driven techniques to improve the performance, efficiency, and adaptability of Silvus’ advanced MIMO radios and wireless networking systems.  This individual will work closely with experts in wireless communications, DSP, networking, and embedded systems to develop ML-driven features that solve real-world problems in dynamic and challenging RF environments. This position is based at Silvus Technologies’ headquarters in the heart of vibrant West Los Angeles, CA, and is on a hybrid schedule.  A minimum of 3 days onsite per week is expected. On-site days are Mondays, Wednesdays, and Thursdays. The following is a list of at least some of the current essential job functions of the position. Management may assign or reassign duties and responsibilities at any time at its discretion. ROLE AND RESPONSIBILITIES Research, design, and implement machine learning algorithms to enhance performance in wireless communication systems (e.g., link adaptation, interference mitigation, anomaly detection, spectrum sensing). Analyze real-world radio frequency datasets to extract insights and develop predictive models. Develop software prototypes and integrate ML algorithms with Silvus’ radio firmware and networking stack. Collaborate with cross-functional teams to define machine learning use cases and evaluate the impact of deployed models. Contribute to the design of data pipelines and infrastructure for training, testing, and validating models. Participate in performance benchmarking and iterative improvement cycles. Stay current with the latest Machine Learning research for wireless and embedded systems. Perform other related duties of which the above are representative. REQUIRED QUALIFICATIONS M.S. or Ph.D. in Electrical Engineering, Computer Science, or a related field. Minimum of 3 years of experience in machine learning, with demonstrated application to real-world problems; 1 year of machine learning experience with a PhD. Strong foundation in supervised and unsupervised learning, signal processing, and statistical modeling. Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, etc.). Familiarity with wireless com ... (truncated, view full listing at source)
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