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Gpu Engineer Jobs in Raleigh, NC (NOW HIRING)

Nvidia

Senior GPU Memory Architect

Durham, NC · On-site

This GPU memory architecture team creates new, innovative products tailored to NVIDIA's world ... Master degree or equivalent experience in Electrical Engineering, Computer Science, Computer ...

Nvidia

Senior GPU Architect

Durham, NC · On-site

$125K - $170K/yr

The NVIDIA GPU Architecture group is looking for world class architects and software developers to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in ...

Nvidia

Senior GPU Architect, Deep Learning

Durham, NC · On-site

The NVIDIA GPU Architecture group is looking for world class architects and software developers to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in ...

SAS

DevOps Engineer

Cary, NC

$49.25 - $67.50/hr

... Engineer, you will ... Maintain GPU-based infrastructure including optimizing GPU utilization for largescale deep learning ...

SAS

DevOps Engineer

Cary, NC · On-site

$49.25 - $67.50/hr

... Engineer, you will ... Maintain GPU-based infrastructure including optimizing GPU utilization for large-scale deep ...

Nvidia

Senior GPU Memory Architect

Durham, NC

NVIDIA has pioneered programmable GPUs and the CUDA language and is a world leader in high ... Develop architecture and micro-architecture features to improve the state-of-the-art in GPU memory ...

Nvidia

Senior Software Engineer, CUTLASS Platform

Durham, NC · On-site

$118K - $156K/yr

Collaborate with GPU architecture, CUDA, and NVVM/PTX compiler teams to provide feedback on programming models and to assess the performance of future GPU hardware features. What we need to see:

Nvidia

Senior ML Platform Engineer

Durham, NC

$101K - $138K/yr

Our invention-the GPU-functions as the visual cortex of modern computing and is central to ... We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning ...

Contemporaries

Be Seen First

Data Engineer

Durham, NC · On-site

$57 - $63/hr

... Data Engineer to support environmental health and extreme weather research initiatives. This ... Develop and test protocols for GPU-accelerated execution of R- and Python-based models. * Create ...

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All JobsGpu Engineer JobsGpu Engineer Jobs in Raleigh, NC

Gpu Engineer information

See Raleigh, NC salary details

$37.9K

$98.9K

$133.7K

How much do gpu engineer jobs pay per year?

As of Jun 14, 2026, the average yearly pay for gpu engineer in Raleigh, NC is $98,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,700.00 and $113,200.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior GPU engineers, especially those with extensive experience, specialized skills in graphics architecture, and leadership roles, can earn $500,000 or more annually. High compensation often includes bonuses, stock options, and other incentives, particularly in large tech companies or specialized industries like gaming, AI, or data centers.

What engineers make $300,000 a year?

Senior GPU engineers, especially those with extensive experience, advanced skills in graphics architecture, and expertise in programming languages like C++ and CUDA, can earn $300,000 or more annually. High-level roles in large tech companies or specialized fields such as AI and machine learning often offer compensation at this level, often including bonuses and stock options.

What jobs pay $400 an hour?

High-paying roles for GPU engineers or related specialized tech positions can reach $400 an hour, typically in consulting, freelance, or contract work for companies needing advanced graphics processing or AI hardware development. Such roles often require extensive experience, advanced skills in hardware design, and a strong portfolio, with some professionals earning this rate through independent consulting or in executive technical positions.

What are the key skills and qualifications needed to thrive in the Gpu Engineer position, and why are they important?

To thrive as a GPU Engineer, you need strong knowledge of computer architecture, proficiency in C/C++, and experience with parallel programming models such as CUDA or OpenCL, along with a degree in computer science, electrical engineering, or a related field. Familiarity with debugging tools, driver development, performance profiling utilities, and hardware simulation platforms is typically required. Excellent problem-solving abilities, attention to detail, and effective teamwork and communication skills help distinguish top candidates. These skills ensure that GPU Engineers can develop high-performance solutions, efficiently troubleshoot hardware and software issues, and collaborate successfully in multidisciplinary environments.

What does a GPU engineer do?

A GPU engineer designs, develops, and optimizes graphics processing units and related hardware or software. They work on improving graphics performance, parallel processing, and computational efficiency, often using programming languages like C++ and tools such as CUDA or OpenCL. Their work supports applications in gaming, scientific computing, and machine learning.

What does a GPU Engineer do?

A GPU Engineer designs, develops, and optimizes graphics processing units (GPUs) for applications like gaming, artificial intelligence, and high-performance computing. They work on hardware architecture, driver development, and parallel computing optimizations to maximize performance. GPU Engineers collaborate with software developers, hardware designers, and researchers to improve graphics rendering, machine learning acceleration, and computational efficiency.

What are some common challenges faced by GPU Engineers, and how are they addressed?

GPU Engineers often face challenges such as optimizing code for maximum parallel efficiency, debugging complex hardware-software interactions, and keeping pace with rapidly evolving GPU architectures. Addressing these issues typically requires a combination of deep architectural understanding, use of specialized profiling and debugging tools, and ongoing collaboration with hardware, software, and QA teams. Many companies provide ongoing training and encourage knowledge sharing within engineering teams to help individuals stay current and effectively tackle new technical hurdles. Overcoming these challenges not only sharpens technical expertise but also opens doors for career growth into architect, team lead, or principal engineer roles.

What are the most commonly searched types of Gpu Engineer jobs in Raleigh, NC? The most popular types of Gpu Engineer jobs in Raleigh, NC are:
What are popular job titles related to Gpu Engineer jobs in Raleigh, NC? For Gpu Engineer jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Gpu Engineer jobs in Raleigh, NC look for? The top searched job categories for Gpu Engineer jobs in Raleigh, NC are:
Gpu Engineer jobs near you
System Software Engineer - Data Center GPU Compute Diagnostics

System Software Engineer - Data Center GPU Compute Diagnostics

Nvidia

Durham, NC

$167K - $198K/yr

Full-time

Posted 24 days ago

Job description

We are seeking a system software engineer to work on next-generation Data Center GPU diagnostics for rack-scale AI supercomputer systems. Our charter is to build applications and compute workloads that test and heavily stress GPU compute engines, HBM memory, cache hierarchy, PCIe/NVLinkinterfaces, power delivery, and thermal behavior, and to use those applications in silicon/system bring-up along with packaging such tools for manufacturing and customer use. In this role you will partner with a senior engineer leading the team's CUDA kernel and GEMM diagnostics work, owning well-scoped pieces of the codebase end-to-end while ramping on GPU microarchitecture and silicon characterization. The best candidates will have experience writing low-level diagnostic, performance, or stress software for complex hardware systems, ideally including experience with GPUs, CUDA kernels, GEMM-style workloads, CPUs,NICsor high-speed interconnects such as PCIe.

Good interpersonal skills arerequiredas this role will involve close collaboration with hardware architecture, silicon validation,manufacturingand field teams. In addition, the engineer will grow their knowledge of operating systems, computer architecture, GPU memory, voltage/frequency behavior, thermal limits, high-speed buses, and modern AI development and analysis tools to efficientlyvalidateand test next-generation processors and systems.Join an exciting,rewardingandfast pacedenvironment!

Whatyou'llbe doing:

  • Working closely with hardware architecture, driver, manufacturing, and field teams through the product development lifecycle of rack-scale AI systems.

  • Implementing andmaintainingCUDA/C++ diagnostic workloads and software infrastructure used in chip development, validation, productization, and field triage.

  • Writing and tuning GPU compute tests that stress Tensor Cores, SMs, L2/cache hierarchy, HBM memory, and related power/thermal operating points.

  • Implementing and tuning GEMM-style diagnostic workloads, including tests combined withadditionalloadinNVLink,PCIe or CPU subsystems.

  • Contributing to higher-level AI workload tests, includingPyTorch-based large model workloads that stress GPUs, memory, interconnects, thermals, and system software under realistic rack-scale AI use cases.

  • Bringing up andvalidatingnew hardware features with pre-beta GPU drivers, low-level diagnostic software, and system telemetry, under guidance from the technical lead.

  • Triaging and debugging failures involving ECC, HBM behavior, thermal limits, voltage/frequency margining, and PCIe/NVLinkerrors.

What we need to see:

  • BS or MS degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent experience.

  • 5+ years of system software, GPU software, embedded software, or hardware validation experience.

  • Experience writing low-level diagnostics, interacting with device firmware and hardware level debuggers.

  • Strong C/C++ and Python programming skills.

  • Exposure to GPU architecture, CUDA kernels, GPU compute workloads, or related accelerator programming is strongly preferred.

  • Working knowledge of memory systems, ECCbehaviorandDMAengines.

  • Familiarity with GEMM-styleworkloads.

  • Awareness of voltage/frequency characterization, thermal testing, power stress, or related silicon validation concepts such asVmin/Fmax and P-state testing.

  • Experience using modern AI development and analysis tools to improve engineering velocity, including code development, debugging, and test creation.

  • Strong problem solving and low-level debugging skills.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 24, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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