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 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.
Preferred Qualifications
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
Educational Qualifications
Bachelor’s/Master’s/PhD degree in Electrical Engineering, Electronics and Communication, Computer Science, or related field.
Minimum Qualifications
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
- 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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