Ambient Compute System Architecture and Power Engineer

Qualcomm

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$148,300–$222,500 / yr
Posted
53 days ago
Freshness
Confirmed live 2 days ago
Closes
Jan 16, 2027

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $201k
This role $185k
$138k most similar roles pay here $246k

This role pays less than 58% of similar roles. Most pay $166,250–$235,750 — the shaded band above. At the midpoint, this role pays about $185k versus about $201k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 623 open roles on FindRole.

Listed pay typically runs $148,300–$222,500 across 603 roles with salary data.

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At a glance

TL;DR · Ambient Compute System Architecture and Power Engineer

As an Ambient Compute System Architecture and Power Engineer within the Low-Power AI Systems group, you will focus on performance optimization, data-path analysis, and architecture for embedded AI subsystems. You will be responsible for analyzing power-performance trade-offs across DSP and eNPU components to support XR and always-on AI workloads. Your daily work involves performing detailed memory-access analysis of cache, SRAM, and DDR to identify bottlenecks, while driving workload partitioning, software optimizations, and data reuse strategies. The role requires expertise in embedded processor architectures, RTOS, and power modeling. You will utilize Python for analysis and automation while employing lab-based power measurement tools to correlate silicon data with models. Key technical domains include fixed-point implementation, algorithm optimization, and managing the power characteristics of ML inference workloads across mobile, compute, and IoT platforms.

What you'll do

  • Analyze and optimize performance and power for DSP, eNPU, and memory subsystems in XR and always-on AI workloads.
  • Perform detailed data-path and memory-access analysis to identify bottlenecks in cache, SRAM, and DDR systems.
  • Drive workload partitioning, software optimizations, clock/bandwidth voting, and data reuse strategies.
  • Execute lab-based power measurements and correlate silicon data with internal models.
  • Support system integration, benchmarking, and commercialization of LPAI solutions across mobile, XR, and IoT platforms.
  • Document performance findings, competitive analysis, and architectural recommendations for internal stakeholders.

What we're looking for

  • Bachelor's degree with 4+ years of experience, Master's with 3+ years, or PhD with 2+ years in Engineering, Computer Science, or a related field.
  • Experience with embedded processor architectures such as DSPs and NPUs including understanding of 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.

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