Machine Learning Engineer, Motorola Solutions

• 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 communication concepts (e.g., PHY/MAC layers, MIMO, OFDM, spectrum access, SDRs)
• Proficiency in MATLAB or C/C++ for signal processing algorithm development
• Security Clearance: Active U.S. Government SECRET clearance or the ability to obtain one within 15 months of hire
• 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
• WORKING CONDITIONS AND PHYSICAL REQUIREMENTS:
• Office environment
• Outdoor environment for demos
• Occasional exposure to heat, cold, and allergens while performing tests or demonstrations in the field
• Lift equipment up to 20 lbs. for the set-up of demonstrations and testing
• Perform bending and reaching movements to place items on lower and higher shelves

Desirable

• 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
• 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