GPU Power Analysis Engineer

Qualcomm

Confirmed live today Trusted

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $169,500–$254,300 / yr
Posted: 40 days ago
Freshness: Confirmed live today
Closes: Mar 16, 2027
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $213k

This role $212k

$138k $280k

below market most similar roles pay here above market

This role pays less than 53% of similar roles. Most pay $185,050–$239,987 — the blue band above. At the midpoint, this role pays about $212k versus about $213k 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 575 open roles on FindRole.

Listed pay typically runs $147,900–$221,900 across 550 roles with salary data.

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At a glance

TL;DR · GPU Power Analysis Engineer

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The GPU Power Analysis Engineer joins the Graphics System Team to influence next generation GPU efficiency and performance. You will perform performance and power efficiency analysis and modeling at both the GPU and system levels for Graphics and AI workloads, such as games and benchmarks. Your daily work involves exploiting various software, hardware, and architectural knobs to improve performance per watt, establishing scalable power projection methodologies for GPU and SOC sizing, and developing tools for streamlined debug and validation tasks. You will utilize Python, signal processing techniques, and AI tools for coding and debugging. The role requires deep knowledge of GPU and SOC architectures, micro-architecture power concepts, and silicon measurements to identify hardware and software enhancement opportunities for future graphics processing units.

Skills

SoC Architecture Python Power Analysis Micro-architecture Post-Si Debugging Signal Processing Graphics APIs Hardware Engineering Systems Engineering AI Tools CPU DDR Computer Engineering

What you'll do

Perform performance and power efficiency analysis for GPU and system-level Graphics/AI workloads.
Identify hardware and software optimization opportunities to improve performance per watt.
Establish scalable methodologies for power projections during next-generation GPU and SOC sizing.
Conduct competitive performance and power analysis on popular benchmarks and games.
Develop tools to streamline and scale debug and validation tasks.
Provide post-silicon learning to improve next-generation architectural models.
Analyze performance and power data from silicon measurements across various SOC components.

What we're looking for

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of relevant engineering experience.
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 3+ years of relevant engineering experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of relevant engineering experience.
Understanding of GPU and/or SOC architecture and common SoC hardware blocks like Display, CPUs, and BUS.
Understanding of power concepts at both the micro-architecture and system architecture levels.
Strong knowledge of Python or other programming languages.
Fluency in using the latest AI tools for coding and debugging system-level problems.
Knowledge of graphics APIs, signal processing techniques, and competitive performance analysis (preferred).

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