Staff Engineer, GPU Memory Architect

Samsung Electronics

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$151,000–$251,800 / yr
Posted
7 days ago
Freshness
Confirmed live yesterday
Closes
Mar 31, 2027

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $211k
This role $201k
$139k most similar roles pay here $265k

This role pays less than 56% of similar roles. Most pay $177,250–$245,000 — the shaded band above. At the midpoint, this role pays about $201k versus about $211k for comparable roles.

Based on 240 similar postings.

Employer

About Samsung Electronics

Samsung Electronics is a South Korean multinational corporation and a global leader in technology, specializing in consumer electronics, semiconductors, and home appliances.

Samsung Electronics currently has 104 open roles on FindRole.

Listed pay typically runs $117,000–$174,500 across 69 roles with salary data.

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At a glance

TL;DR · Staff Engineer, GPU Memory Architect

Staff Engineer, GPU Memory Architect joins the GPU Architect team to drive the definition, architecture, and implementation of next-generation GPU memory subsystems and on-chip interconnects for Exynos chipsets. This role involves leading architectural studies for global memory hierarchies, cache structures, and high-bandwidth memory interfaces while optimizing performance, power, and area for mobile and AI workloads. The candidate will build and maintain performance-power-area models, conduct trade-studies for ray-tracing and AI inference, and develop design specifications and validation plans. Key technical requirements include expertise in GPU architecture, memory hierarchies like SRAM and DRAM, coherence protocols, and low-power design techniques. Necessary skills include proficiency in RTL-level simulation, analytical modeling, and hardware-software co-design. The role addresses the challenge of integrating complex memory blocks into system-on-chip designs to support advanced graphics and machine learning applications.

What you'll do

  • Define and architect next-generation GPU memory subsystems and on-chip interconnects for Exynos chipsets.
  • Lead architectural studies and micro-architecture development for global memory hierarchies, cache structures, and high-bandwidth interfaces.
  • Optimize performance, power, and area (PPA) for GPUs supporting mobile and AI workloads.
  • Build and maintain performance-power-area models to conduct trade-studies for ray-tracing and AI inference.
  • Translate algorithmic requirements from graphics and machine learning into specific memory-system specifications.
  • Produce architectural roadmaps, design specifications, and validation plans to drive data-driven decision making.
  • Ensure the seamless integration of memory blocks into the overall system-on-chip (SoC) architecture.

What we're looking for

  • Bachelor's degree in Computer Science or Engineering with 6+ years of experience, Master's with 4+ years, or Ph.D. with 2+ years.
  • 6+ years of experience in GPU architecture, design, or development focusing on high-performance and low-power designs.
  • Knowledge of machine learning, graphics, GPU pipelines, hardware design, and computer architecture.
  • Proven track record of analyzing and optimizing memory subsystems to improve performance, power, and area (PPA).
  • Deep knowledge of memory hierarchies, bandwidth-allocation policies, coherence protocols, and on-chip interconnects.
  • Strong background in low-power design techniques and performance-modeling tools like analytical models or RTL-level simulation.
  • Experience with emerging GPU workloads such as ray tracing, AI/ML inference, and AR/VR.
  • Authorization to access information subject to U.S. export control laws.

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