Machine Learning Engineer

Motorola Solutions•Los Angeles, CA

15d•$100,000 - $140,000•Hybrid

About The Position

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.

Requirements

Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 2 years of experience in machine learning, with demonstrated application to real-world problems; no experience required with an advance degree (MS or PhD)
Strong foundation in supervised and unsupervised learning and statistical modeling.
Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, etc.).
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.

Nice To Haves

M.S. or Ph.D. in Electrical Engineering, Computer Science, or a related field.
Demonstrated experience with RF signal classification, anomaly detection, or spectrum monitoring.
Proficiency in MATLAB or C/C++ for signal processing algorithm development.
Familiarity with wireless communication concepts (e.g., PHY/MAC layers, MIMO, OFDM, spectrum access).
Familiarity with embedded ML, real-time systems, or deploying ML on edge devices.
Background in adaptive modulation, beamforming, or cognitive radio techniques.
Experience working with wireless standards such as 3GPP, IEEE 802.11/15, or military waveforms.
Experience with GPU acceleration or model optimization for constrained environments.
Excellent communication and collaboration skills.

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 RF 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 ML 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.