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Gpu Computing Jobs (NOW HIRING)

Jobtailor

Senior System Engineer - GPU Platforms

San Jose, CA · On-site

$122K - $167K/yr

Experience with GPU computing, accelerators, or comparable high-performance computing technologies * Ability to independently manage complex technical assignments and drive issues toward resolution

Supermicro

Sr. System Engineer/GPU Platforms

San Jose, CA · On-site

$123K - $169K/yr

This role focuses on multi-GPU server systems used for AI, HPC, enterprise computing, and accelerated workloads. The successful candidate will work across the product lifecycle, from initial system ...

VTG

High-Performance Computing Engineer

Mclean, VA · On-site

The ideal candidate will bring deep technical expertise in large-scale HPC environments, cluster management, GPU computing, and high-speed networking. Do you have what it takes? * Active Top Secret ...

VTG

High-Performance Computing Engineer

Mclean, VA · On-site

The ideal candidate will bring deep technical expertise in large-scale HPC environments, cluster management, GPU computing, and high-speed networking. Do you have what it takes? * Active Top Secret ...

NVIDIA Corporation

$89K - $120K/yr

... computing. In the last decade, Python has become the de-facto programming language for ... NVIDIA has been at the forefront of providing GPU-accelerated implementations of the fundamental ...

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How much do gpu computing jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for gpu computing in the United States is $18.28, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $19.71 per hour, depending on experience, location, and employer.

What is GPU computing?

GPU computing refers to the use of a Graphics Processing Unit (GPU) alongside a Central Processing Unit (CPU) to accelerate computational tasks. GPUs are highly efficient at performing parallel operations, making them ideal for complex calculations in fields like machine learning, scientific simulations, and graphics rendering. Unlike traditional CPUs, GPUs can process thousands of threads simultaneously, greatly speeding up tasks that involve large-scale data processing. This makes GPU computing essential in industries requiring high-performance computing solutions.

What are some common challenges faced by GPU computing professionals when optimizing code for parallel processing?

One of the main challenges in GPU Computing is efficiently restructuring code to leverage the massive parallelism that GPUs offer. Professionals often encounter issues with memory management, synchronization between threads, and minimizing data transfer between CPU and GPU to avoid bottlenecks. Additionally, debugging parallel code can be complex, as errors may not manifest consistently across runs. Collaborating with software engineers, data scientists, and hardware specialists is typical to ensure optimal performance and scalability in real-world applications.

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

To thrive as a GPU Computing Specialist, you need expertise in parallel programming, computer architecture, and a strong foundation in mathematics and algorithms, often supported by a degree in computer science, engineering, or related fields. Familiarity with programming languages like C/C++, CUDA, OpenCL, and experience with GPU hardware and high-performance computing systems are essential. Problem-solving abilities, analytical thinking, and strong collaboration skills help you innovate and work effectively on complex computational projects. These skills ensure efficient development, optimization, and deployment of GPU-accelerated solutions crucial for scientific, engineering, and AI applications.

What is the difference between Gpu Computing vs Data Scientist?

AspectGpu ComputingData Scientist
Required CredentialsKnowledge of GPU architectures, programming skills in CUDA or OpenCLDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentHigh-performance computing environments, data centers, research labsOffice settings, research institutions, tech companies
Industry UsageMachine learning, scientific simulations, graphics renderingData analysis, predictive modeling, business insights

Gpu Computing focuses on leveraging GPU hardware for high-speed processing tasks, often requiring specialized programming skills. Data Scientists analyze data to extract insights, using various tools and statistical methods. While both roles involve data and computing, Gpu Computing is more hardware and performance-oriented, whereas Data Scientists focus on data analysis and modeling.

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What states have the most Gpu Computing jobs?

States with the most job openings for Gpu Computing jobs include:

What job categories do people searching Gpu Computing jobs look for?

The top searched job categories for Gpu Computing jobs are:

What other helpful pages are available for Gpu Computing?

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Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution, with an average salary of $38,016 per year, or $18.3 per hour.

Senior System Engineer - GPU Platforms

San Jose, CA • On-site

Jobtailor
See more jobs at Jobtailorexternal

$122K - $167K/yr

Other

Posted 7 days ago

Job description

  • Support system bring-up, configuration, integration, validation, and troubleshooting of advanced GPU server platforms
  • Execute and support GPU platform qualification activities, including NVIDIA NVQUAL or equivalent validation processes
  • Install, configure, and troubleshoot Linux, GPU drivers, CUDA environments, firmware, libraries, and related software components
  • Diagnose complex system issues using logs, telemetry, diagnostics, and vendor tools, and drive issues to resolution or appropriate engineering escalation
  • Support multi-GPU server platforms throughout qualification, product launch, and post-release engineering activities
  • Participate in customer-facing POC/EVAL engagements, including system preparation, technical calls, debugging, and issue resolution
  • Collaborate with Architecture, Systems, Software, Validation, Product Management, other engineering teams, and external technology partners
  • Develop technical documentation, troubleshooting guides, and best practices
  • Deliver technical presentations, training sessions, and internal knowledge-sharing activities
  • Serve as a technical resource and mentor for other engineers when appropriate
Requirements
  • Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science, Information Technology, or a related discipline, or equivalent practical experience
  • 5–15 years of relevant industry experience in systems engineering, server engineering, platform engineering, validation, technical enablement, HPC, AI infrastructure, or a related field
  • Strong knowledge of enterprise server hardware and system architecture
  • Hands-on experience with Linux server environments
  • Experience installing, configuring, validating, and troubleshooting server hardware and software
  • Strong system-level troubleshooting and root-cause-analysis skills
  • Working knowledge of PCIe architectures and high-performance I/O
  • Experience with GPU computing, accelerators, or comparable high-performance computing technologies
  • Ability to independently manage complex technical assignments and drive issues toward resolution
  • Strong written and verbal communication skills
  • Ability to work effectively with cross-functional and geographically distributed engineering teams
  • Comfortable participating in customer-facing technical discussions
  • Preferred: Hands-on experience with NVIDIA data center or professional GPU platforms
  • Preferred: Experience with CUDA and NVIDIA GPU software environments
  • Preferred: Experience with NVIDIA NVQUAL or similar platform qualification processes
  • Preferred: Experience with 4-GPU or 8-GPU server platforms
  • Preferred: Familiarity with NVIDIA Blackwell, B200, Rubin, or comparable accelerator architectures
  • Preferred: Knowledge of PCIe topology, NUMA, DMA, IOMMU, and GPU-to-NIC communication
  • Preferred: Experience with GPUDirect RDMA, InfiniBand, RoCE, or high-speed Ethernet
  • Preferred: Familiarity with NCCL, NVML, DCGM, Fabric Manager, or similar GPU diagnostic and management tools
  • Preferred: Experience with Docker, containers, Kubernetes, or related orchestration technologies
  • Preferred: Experience supporting AI, machine learning, HPC, or accelerated computing environments
  • Preferred: Experience with customer POCs, technical evaluations, or engineering escalations
  • Preferred: Experience delivering technical training or knowledge-sharing sessions
  • Bash, Python, or other scripting experience is a plus
Core Competencies

Demonstrates expertise in GPU server platform support, including installation, configuration, and troubleshooting of Linux environments and GPU technologies. Proficient in system-level diagnostics, technical documentation, and cross-functional collaboration to drive complex technical assignments to resolution.

Highest-signal resume keywords
  • GPU Computing
  • Linux Server Environments
  • System-Level Troubleshooting
  • NVIDIA NVQUAL
  • Technical Documentation
Hard Skills
  • GPU Drivers
  • CUDA
  • Server Hardware Configuration
  • Root-Cause Analysis
  • PCIe Architectures
  • High-Performance Computing
  • NVIDIA Data Center Platforms
  • Docker
  • Python Scripting
  • Bash Scripting
Soft Skills
  • Strong Communication Skills
  • Cross-Functional Collaboration
  • Customer-Facing Technical Discussions
  • Mentoring
Industry Keywords
  • Systems Engineering
  • Server Engineering
  • Platform Engineering
  • Technical Enablement
  • AI Infrastructure
  • HPC
Tools & Technologies
  • NCCL
  • NVML
  • DCGM
  • Fabric Manager
  • InfiniBand
  • RoCE
  • High-Speed Ethernet
  • Kubernetes
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