R&D Internship, Motorola Solutions

• In-progress Bachelor’s degree or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a closely related technical field
• Proficiency in a major programming language for data science (e.g. Python) and familiarity with common data manipulation and ML libraries (e.g., Pandas, NumPy, Scikit-learn)
• Basic understanding of statistics, probability, and linear algebra
• 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 testing
• WORKING CONDITIONS & PHYSICAL REQUIREMENTS:
• Office environment
• Trade shows
• Outdoor environment for demonstrations
• Occasional exposure to heat, cold, and allergens while performing tests or demonstrations in the field
• While performing the duties of this job, the employee is required to do the following:
• 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
• Kneeling or squatting to access lower shelves
• Must be able to lift up to 50 lbs. when receiving incoming part shipments
• Walking/Moving in the labs

Desirable

• The Intern – Machine Learning Engineer reports to the R&D Director on the Machine Learning team with dotted line reporting to the R&D Engineer
• The successful individual in this role will learn from and work with experts in wireless communications, DSP, networking, and embedded systems to help solve real-world problems in dynamic and challenging RF environments
• This internship is part-time, on-site Monday through Friday, at Silvus Technologies’ HQ in the heart of vibrant West LA
• The following is a list of at least some of the current essential functions of this internship opportunity
• Develop, train, and evaluate machine learning and deep learning models for various applications related to wireless communication and signal processing
• Design and implement data processing and feature engineering pipelines to prepare complex radio and sensor data for model training
• Perform exploratory data analysis (EDA) and statistical modeling to uncover insights and patterns in large datasets
• Collaborate with the R&D and Systems Engineering teams to integrate ML solutions into hardware and software prototypes
• Write custom scripts and tolls for automated data capture, analysis, and visualization
• Participate in the generation of technical documentation and presentations outlining research and development findings
• Opportunities to further engage, learn, and develop skills as projects progress