Senior Engineer- Power Architecture and Systems Engineer

Qualcomm · Hyderabad · 2+ yrs experience · Posted 2026-06-21

Tech stack: Python

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About the role

Qualcomm India Private Limited
Engineering Group, Engineering Group > Systems Engineering
Responsibilities: - 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/ eNPU subsystems by analyzing power-performance trade-offs, and enabling optimizations across memory access, data movement, and workloads for on-device AI.
- Analyze and optimize power consumption of LPAI subsystems (DSP eNPU memory hierarchy) with focus on XR and always-on AI workloads.
- Develop system-level power analysis to evaluate different audio use cases across DSP abd eNPU
- Perform detailed data-path and memory-access analysis (TCM, LLC, DDR) to identify bottlenecks impacting power efficiency.
- Drive power optimization techniques including clock/BW voting, workload partitioning, scheduling, and data reuse strategies.
- Collaborate with HW, SW, and PdM teams to review eNPU power 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.
Qualifications: - Strong fundamentals in power modeling, power analysis, and system-level power optimization.
- Experience with embedded processor architectures such as DSPs and NPUs, with understanding of eNPU power 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, and Cache memory strategies.
- 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.
- Experience with Qualcomm DSP and LPAI architectures, SDKs, or internal power tools.
- Background in audio or always-on AI use cases.
- Exposure to ML inference workloads and their power-performance characteristics
- Bachelor’s/Master’s/PhD degree in Electrical Engineering, Electronics and Communication, Computer Science, or related field.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field.

Qualifications

- Strong fundamentals in power modeling, power analysis, and system-level power optimization.
- Experience with embedded processor architectures such as DSPs and NPUs, with understanding of eNPU power 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, and Cache memory strategies.
- 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.
- Experience with Qualcomm DSP and LPAI architectures, SDKs, or internal power tools.
- Background in audio or always-on AI use cases.
- Exposure to ML inference workloads and their power-performance characteristics
- Bachelor’s/Master’s/PhD degree in Electrical Engineering, Electronics and Communication, Computer Science, or related field.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field.

Responsibilities

- 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/ eNPU subsystems by analyzing power-performance trade-offs, and enabling optimizations across memory access, data movement, and workloads for on-device AI.
- Analyze and optimize power consumption of LPAI subsystems (DSP eNPU memory hierarchy) with focus on XR and always-on AI workloads.
- Develop system-level power analysis to evaluate different audio use cases across DSP abd eNPU
- Perform detailed data-path and memory-access analysis (TCM, LLC, DDR) to identify bottlenecks impacting power efficiency.
- Drive power optimization techniques including clock/BW voting, workload partitioning, scheduling, and data reuse strategies.
- Collaborate with HW, SW, and PdM teams to review eNPU power 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.

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