Ambient Compute System Architecture and Power Engineer

* Analyze and optimize performance and power of LPAI subsystem (DSP, eNPU, memory) with focus on XR and always-on AI workloads.

* Support system integration, benchmarking, and commercialization of LPAI solutions across Mobile, XR, Compute, and IoT platforms.

* Perform detailed data-path and memory-access analysis (cache, SRAM, DDR) to identify bottlenecks impacting performance efficiency.

* Drive ambient system workload partitioning, software optimizations, clock/BW voting, and data reuse strategies.

* Collaborate with HW, SW, and PdM teams to review low-power feature roadmap.

* Execute lab-based power measurements, correlate silicon data with modelling.

* Document performance and power analysis, competitive analysis findings, and architectural recommendations for internal stakeholders.

- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

* Experience with embedded processor architectures such as DSPs and NPUs, with understanding of processor power behavior.

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

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

* Solid understanding of memory systems, data movement, bandwidth analysis, and Cache memory strategies.

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

* Hands-on experience with power measurement tools, and data analysis techniques

* Knowledge of fixed-point implementation and algorithm optimization techniques.

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

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

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