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Gpu Performance Engineer Jobs in Rancho Cucamonga, CA

Hornet Acquisition Co LLC

Senior Buyer

Anaheim, CA · On-site

$72K - $109K/yr

Signia Aerospace is a global, integrated provider of high-performance systems and specialized ... Partner with engineering, production, and quality teams to align procurement strategies with ...

Hornet Acquisition Co LLC

Program Manager

Anaheim, CA · On-site

$150K - $170K/yr

Signia Aerospace is a global, integrated provider of high-performance systems and specialized ... Translate customer requirements into executable plans across engineering, manufacturing, supply ...

All JobsGpu Performance Engineer JobsGpu Performance Engineer Jobs in Rancho Cucamonga, CA

Gpu Performance Engineer information

See Rancho Cucamonga, CA salary details

$11

$61

$100

How much do gpu performance engineer jobs pay per hour?

As of Aug 4, 2026, the average hourly pay for gpu performance engineer in Rancho Cucamonga, CA is $61.42, according to ZipRecruiter salary data. Most workers in this role earn between $50.34 and $69.52 per hour, depending on experience, location, and employer.

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

What are the key skills and qualifications needed to thrive as a GPU performance engineer, and why are they important?

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What job categories do people searching Gpu Performance Engineer jobs in Rancho Cucamonga, CA look for? The top searched job categories for Gpu Performance Engineer jobs in Rancho Cucamonga, CA are:
What cities near Rancho Cucamonga, CA are hiring for Gpu Performance Engineer jobs? Cities near Rancho Cucamonga, CA with the most Gpu Performance Engineer job openings:
Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in Rancho Cucamonga, CA as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $127,759 per year, or $61.4 per hour.

Senior Solution Architect - AI / GPU Cloud (Mountain View)

GMI Cloud

Victorville, CA • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Senior Solution Architect – AI / GPU Cloud

We are seeking a Senior Solution Architect to design GPU‑cloud and AI infrastructure solutions, lead PoCs and benchmarks, guide customers through deployment, and partner closely with engineering and operations teams at GMI Cloud.

About GMI Cloud

GMI Cloud is a fast‑growing AI infrastructure company backed by Headline VC. We operate hundreds of megawatts of AI‑ready data center capacity across North America and a growing AI Factory footprint in Asia, delivering a full spectrum of services from GPU compute to AI model inference API solutions. As an NVIDIA Reference Platform Cloud Partner, our infrastructure meets the highest standards for performance, security, and scalability in AI deployments.

Role Overview

As a Solution Architect, you will be the primary technical interface for our enterprise and hyperscaler accounts and help customers build AI without limits.

Key Responsibilities
  • Serve as the primary technical point‑of‑contact for enterprise and hyperscaler customers.
  • Deeply understand customer AI/ML/HPC workloads, scaling requirements, and deployment models.
  • Architect GPU clusters, storage, networking, and orchestration solutions tailored to customer needs.
  • Lead proof‑of‑concepts, benchmarks, and workshops demonstrating performance, reliability, and scalability.
  • Produce technical proposals, architecture diagrams, capacity plans, and cost/performance recommendations.
  • Translate complex technical issues into clear actions for both engineering and business stakeholders.
  • Guide customers through onboarding, cluster setup, performance tuning, and scaling.
  • Partner with internal infra, DC ops, and engineering teams to ensure smooth delivery and implementation.
  • Identify optimization opportunities in customer workloads (GPU utilization, networking, scheduling, cost).
  • Act as a trusted advisor on GPU/AI infrastructure best practices, roadmap, and long‑term planning.
  • Maintain regular technical check‑ins, capacity reviews, and performance reviews with customers.
  • Gather customer feedback and collaborate with product/engineering to improve our platform.
Required Qualifications Technical Background
  • 5–10+ years in cloud infrastructure, GPU cloud, HPC, AI/ML infrastructure, or data center engineering.
  • Strong understanding of distributed training & inference architectures, Kubernetes, Slurm, or other cluster/orchestration systems, NVIDIA GPU stack (H100/H200/B200/GB200 or similar), InfiniBand/high‑speed networking, and storage architectures for AI workloads.
Customer‑Facing Skills
  • Experience working directly with enterprise or hyperscaler technical teams.
  • Ability to simplify complex infra concepts for both technical and non‑technical audiences.
  • Strong communication, solution‑design, and project coordination skills.
Soft Skills
  • Self‑starter, ownership mindset, excellent follow‑through.
  • Comfortable working in a fast‑moving, high‑growth environment.
  • Strong problem‑solving and “architect + advisor” mentality.
Preferred Qualifications (Nice to Have)
  • Hands‑on with large‑scale GPU deployments (multi‑node, multi‑cluster).
  • Exposure to hyperscaler capacity planning or AI infrastructure procurement teams.
  • Experience with multi‑region or global GPU deployments (US + APAC/Taiwan).
Why Join GMI Cloud
  • Work directly with some of the world’s most advanced AI organizations.
  • Architect and deliver multi‑MW GPU clusters at global scale.
  • Influence product roadmap and partner closely with NVIDIA and top‑tier data center providers.
  • High‑impact role with significant ownership and career growth.
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