DSP/AI/ML Staff Engineer - Embedded Power Management

Responsibilities

• Leads the definition and development of embedded ML algorithms for power management applications
• Contributes to the technical development of embedded ML algorithms, including data collection, model training, model testing, and deployment
• Contributes to the development of custom software and hardware to support ML applications
• Aligns with business unit team on deployment and roadmaps in support of corporate strategies to enable ML applications
• Coordinates with research and engineering teams on ML tools and workflows
• Facilitates support and ML training for engineering and applications teams
• Ensures security and compliance with relevant corporate standards and regulations

Key Requirements :

• Master’s or Ph.D. degree in computer science, electrical or computer engineering or related field
• 10-15 years of experience in ML development role, preferably in battery management or motor control applications
• Knowledge of commercial and open-source ML packages and AutoML tools
• Experience with common embedded ML models SVN, CNN, DNN, kNN, K-means, decision trees, random forests, ensemble methods, and familiarity with the math behind ML
• Familiar with ML data flows, pipelines, and MLOps development cycles
• A keen interest in ML and the ability to lead efforts to quickly prototype and evaluate the performance of ML powered solutions
• Experience with signal processing algorithms and implementation
• Understanding of microprocessor and microcontroller systems
• Experience and understanding of battery management systems, fuel gauging techniques
• Experience and understanding of motor control systems, motor fault detection
• Software experience with C, python, and Matlab
• Ability to quickly master new technologies, languages, and development environments as needed
• Excellent communication and problem-solving skills with the ability to work with customers and across business and engineering teams

Preferred :

• Understanding of digital and analog signal processing techniques such as filtering, spectrum analysis, A / D and D / A conversion, and statistical signal analysis
• Understanding of fault and anomaly detection, predictive and prescriptive maintenance
• Experience developing commercial ML applications, particularly in battery-powered devices
• Experience in developing embedded software for ARM Cortex M class microprocessors
• Semiconductor or electronics industry experience, particularly in battery management or motor control
• Understanding of battery fuel gauging, state-of-charge, and state-of-health estimation
• Understanding of factors affecting battery performance including chemistry, temperature, aging, load rate
• Understanding of DC motors, including BLDC and PMSM, and FOC motor control
• Electrical engineering or hardware design experience

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