General Summary:
Qualcomm is looking for an experienced Chipset Power System Engineer who is passionate in solving power challenges and develop innovative solutions for optimizing power for next generation Snapdragon automotive chipsets.
The engineer is expected to lead a cross-functional engineering team to model SOC/chipset power and come up with innovative solutions to optimize hardware and software to enhance SOC and chipset and achieve world-class chipset low power consumption.
In this position, the engineer will be involved in all stages of the design and development cycles guiding the power-efficient design of the end-end solution.
Responsibilities include:
-- Power architecture and tradeoff analysis
-- Chipset and system level power modeling, analysis and optimization for SOC, chipset and platform solutions.
-- Identify power requirements and quantify power gap/risk areas.
-- Optimize power efficiency and performance of present generation designs and help architect future products.
-- Guide multi-function groups through the design cycle in realizing the power targets set forth at the inception of the project.
-- Design of power features and work with cross functional teams (systems, software, hardware, silicon process, production test, etc.).
-- Customer interaction to advertise the capabilities of the chipset, and to understand and address specific power requirements.
-- Power modeling of customer-specific use cases and designs.
Minimum Qualifications:
A Master’s degree in Electrical Engineering or equivalent field is required as a minimum. A minimum of 10 years of professional experience in one or more of the following areas is preferred.
-- Analysis and design of low power features at SOC, chipset or platform level.
-- Architectural knowledge in application processor, modem processor, AR/XR, machine learning, automotive or computing.
-- System knowledge of automotive SOC or chipset.
-- Hardware or software design or optimizations of low power features.
-- System level power modeling and analysis.
-- Understanding of machine learning networks for Automotive applications (ADAS, IVI and GENAI)
Minimum Qualifications:
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