Systems Architect: Memory Wall Exploration

HP Inc.

Quick summary

Work type
On-site
Location
Spring, TX
Salary
$147,050–$230,850 / yr
Posted
13 days ago
Closes
Dec 2, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $205k
This role $189k
$134k most similar roles pay here $266k

This role pays less than 63% of similar roles. Most pay $173,775–$235,750 — the shaded band above. At the midpoint, this role pays about $189k versus about $205k for comparable roles.

Based on 240 similar postings.

Employer

About HP Inc.

HP Inc. is a global technology company that develops and sells personal computers, printers, and related supplies and services. Its products include laptops, desktops, workstations, and printing solutions for consumers and businesses worldwide.

HP Inc. currently has 79 open roles on FindRole.

Listed pay typically runs $130,700–$205,200 across 76 roles with salary data.

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At a glance

TL;DR · Systems Architect: Memory Wall Exploration

The Systems Architect for Memory Wall Exploration at HP is a senior-level role focusing on analyzing how AI workloads interact with system resources across CPU, GPU, and AI accelerators. This architect will evaluate memory bandwidth, latency, contention, and data movement to optimize performance and power efficiency in shared-memory systems. Key responsibilities include translating technical constraints into actionable guidance for platform architecture teams, collaborating with external partners like memory and silicon vendors, and defining internal tools and metrics for assessing AI workload behavior. The ideal candidate has a strong background in computer engineering or electrical engineering, experience with machine learning hardware acceleration, and proficiency in C/C++ and Python. They should also be familiar with simulation tools such as GEM5 and Sniper, and have expertise in client platforms like PCs and mobile devices, making this role crucial for enhancing AI experiences across HP’s diverse product portfolio.

What you'll do

  • Analyze AI workloads' interaction with memory hierarchy and data movement across CPU, GPU, and accelerators.
  • Evaluate the impact of memory bandwidth, latency, and contention on workload performance and power efficiency.
  • Characterize shared-memory system behavior and identify sources of performance variability under multitasking scenarios.
  • Translate technical constraints into actionable guidance for platform architecture and runtime teams.
  • Engage with external partners to understand memory technologies' impact on AI experiences and provide HP-specific recommendations.

What we're looking for

  • Strong background in systems performance and platform architecture.
  • Solid understanding of memory hierarchy, data movement, and shared-memory systems.
  • Experience with machine learning hardware acceleration and processing-in-memory techniques.
  • Knowledge of low-power design and heterogeneous compute environments (CPU, GPU).
  • Ability to translate technical constraints into system-level guidance and strategy.
  • Proficiency in C/C++ and scripting languages for modeling and analysis.
  • Experience collaborating across hardware, software, and product organizations.

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