As a member of the SoC (System-on-Chip) Low-Power team, you work on a wide range of SoCs spanning different process technologies [40nm-5nm and below] and market segments [IoT, Industrial, Automotive, Networking, Radar] to produce the best-in-class combination of power, performance, and thermal behavior. The scope of the role extends from device to architecture-level optimizations as well as hardware/software co-design.

NXP designs a very wide range of SoC solutions from very cost-sensitive, low-power devices to highly integrated, high-performance devices with a worldwide network of design centers. You will be engaging with NXP's global design team in a highly visible position where strong verbal and written communication skills are a must.

Responsibilities:

• Research, design, and develop forward-looking methodologies, novel infrastructures, and architectures for system resource control and holistic energy management
• Collaborate in system-level analysis of workloads and use cases with performance, thermal, and system teams to identify opportunities for energy optimization
• Identify new techniques in areas for Energy reduction for CPU/GPU/Camera/DDR/ML accelerators both at the core and SoC level
• Experiment with new EDA tools and provide necessary automation to simplify system tradeoff analysis
• Participate in efforts to develop and prototype new system solutions and approaches

Preferred Qualifications:

• Master's or PhD degree in Electrical Engineering or Computer Engineering.
• 12+ years of experience in SOC Low power.
• Strong understanding of digital hardware design fundamentals, including device physics, circuit analysis, and memory usage at the circuit and system level
• Ability to perform CMOS-based power and performance optimization and tradeoffs
• Verilog/SystemVerilog and industry-standard frontend and backend Tools
• Knowledge of reliability [both at circuit and system level]
• Experience with Python, C/C++, and SystemC with strong coding fundamentals
• Familiarity with ML frameworks like Pytorch, caffe, TensorFlow

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