Machine Learning Associate Engineer, Motorola Solutions

• Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 0-2 years of experience in machine learning, with demonstrated application to real-world problems; no experience required with a graduate degree (M.S. or Ph.D.)
• Strong foundation in supervised and unsupervised learning and statistical modeling
• Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, etc.)
• Basic exposure to MATLAB or C/C++ for signal processing algorithm development
• Must be a U.S. Citizen due to clients under U.S. government contracts
• All employment is contingent upon the successful clearance of a background check and drug test

Desirable

• Silvus is seeking an Associate Engineer, Machine Learning who will report to the Research and Development (R&D) Director, Machine Learning on the Research and Development (R&D) team
• The successful individual in this role focuses on supporting machine learning and data-driven techniques to improve the performance, efficiency, and adaptability of Silvus’ advanced MIMO radios and wireless networking systems
• This role works closely under the guidance of experts in wireless communications, DSP, networking, and embedded systems to develop machine learning (ML) driven features that solve real-world problems in dynamic and challenging RF environments
• 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:
• Lead and assist in the 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 (RF) datasets under senior guidance to extract insights and develop predictive models
• Contribute to software prototypes and assist in integrating ML algorithms with Silvus’ radio firmware and networking stack
• Collaborate with cross-functional teams to understand ML use cases and evaluate the impact of deployed models
• Contribute to the design and maintenance of data pipelines and infrastructure for training, testing, and validating models
• Participate in performance benchmarking and iterative improvement cycles
• Stay current with the latest ML research on wireless and embedded systems
• Make informed technical decisions regarding model development, testing strategies, and implementation approaches, escalating issues and recommendations as appropriate
• Perform other related duties of which the above are representative