Interdisciplinary System Engineer, Devices & Services Energy
Core
Design and deploy ML-driven algorithms for battery management, energy storage optimization, and time-series forecasting to optimize battery dispatch and energy usage for new Amazon devices.
Role type
Senior IC interdisciplinary system engineer (battery systems & embedded ML)
Builds
End-to-end battery system architecture, BMS algorithms, and forecasting systems for embedded/edge devices
Domain
Consumer electronics, energy storage, embedded AI/ML
Deliverable
production ML models
Required skills
Battery management systems (BMS), time-series forecasting, control systems, embedded/edge compute, power electronics, hardware architecture, ML-driven algorithms, reinforcement learning, control theory, statistical modeling
Preferred skills
AI/ML technologies, battery electro-chemistry, energy systems beyond batteries
Technologies
GenAI, sensors, smart home devices, cloud services, Alexa
Responsibilities
Own end-to-end battery system architecture from concept to mass production; design and deploy ML-driven algorithms for autonomous battery optimization; develop forecasting systems integrating usage data and grid signals; lead design reviews and validation plans with battery vendors; drive technical trade-offs between algorithm fidelity, compute constraints, and cost; establish experimentation frameworks to validate model performance
Seniority
Senior, hands-on IC