Senior Engineer- Power Architecture and Systems Engineer

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General Summary:

As part of Qualcomm’s Audio and Low-Power AI (LPAI) Systems group, this role focuses on power and data-path analysis, optimization, and architecture of embedded AI subsystems, with emphasis on XR and always-on use cases. The engineer will drive power-efficient system design and analysis across DSP/eNPUsubsystems by analyzing power-performance trade-offs, and enabling optimizations across memory access, data movement, and workloads for on-device AI.

Key Responsibilities

• Analyze andoptimizepower consumption of LPAI subsystems (DSP,eNPU, memory hierarchy) withfocuson XR and always-on AI workloads.

• Develop system-level power analysis to evaluate different audio use cases across DSPabdeNPU,

• Perform detailed data-path and memory-access analysis (TCM, LLC, DDR) toidentifybottlenecksimpactingpower efficiency.

• Drive power optimization techniquesincludingclock/BW voting, workload partitioning, scheduling, and data reuse strategies.

• Collaborate with HW, SW, andPdMteams to revieweNPUpower architecture and low-power feature roadmap.

• Execute lab-based power measurements, correlate silicon data with modelling, and propose optimization strategies.

• Support system integration, benchmarking, and commercialization of power-optimized LPAI solutions across Mobile, XR, Compute, and IoT platforms.

• Document power analysis methodologies, findings, and architectural recommendations for internal stakeholders.

Requirements

• Strong fundamentals in power modeling, power analysis, and system-level power optimization.

• Experience with embedded processor architectures such as DSPs and NPUs, with understanding ofeNPUpower behavior.

• Hands-on experience with power measurement setups such as Kratos, tools, and data analysis techniques

• Strong programming skills in Python for analysis, modeling, and automation.

• Solid understanding of memory systems, data movement, bandwidth analysis, andCache memorystrategies.

• Experience working with embedded platforms, RTOS, and performance/power profiling tools.

• Knowledge of fixed-point implementation and low-power optimization techniques.

• Ability to work across cross-functional and geographically distributed teams.

Preferred Qualifications

• Experience with Qualcomm DSP and LPAI architectures, SDKs, or internal power tools.

• Backgroundin audio, oralways-onAI use cases.

• Exposure to ML inference workloads and their power-performance characteristics

Educational Qualifications

Bachelor’s/Master’s/PhD degree in Electrical Engineering, Electronics and Communication, Computer Science, or related field.

Minimum Qualifications:

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field.

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