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Cuda Engineer Jobs in Raleigh, NC (NOW HIRING)

DevOps Engineer

Cary, NC

$49.25 - $67.50/hr

About the job The Applied AI & Modeling (AAIM) Division seeks a Software Engineer skilled in GPU ... Knowledge CUDA enabled systems and GPU scheduling * Technology Savvy- Leveraging one's practical ...

DevOps Engineer

Cary, NC · On-site

$49.25 - $67.50/hr

About the job The Applied AI & Modeling (AAIM) Division seeks a Software Engineer skilled in GPU ... Knowledge CUDA enabled systems and GPU scheduling * Technology Savvy- Leveraging one's practical ...

Senior Developer Technology Engineer - AI

Durham, NC · Hybrid

$52.75 - $69.50/hr

A background that includes parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, etc. * Hands on experience doing low-level performance optimizations. * In-depth expertise with CPU and ...

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Cuda Engineer information

See Raleigh, NC salary details

$35.5K

$104.3K

$133.7K

How much do cuda engineer jobs pay per year?

As of Aug 5, 2026, the average yearly pay for cuda engineer in Raleigh, NC is $104,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $132,200.00 per year, depending on experience, location, and employer.

What is a CUDA engineer?

CUDA Engineers are software developers who specialize in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to write programs that run on Graphics Processing Units (GPUs). They optimize and accelerate computational tasks by parallelizing code, making use of GPUs’ capabilities for high-performance computing. CUDA Engineers often work in fields like machine learning, scientific computing, and graphics, where large amounts of data need to be processed quickly. Their expertise includes proficiency in C/C++, CUDA programming, and understanding GPU hardware and parallel computing concepts.

What is the difference between Cuda Engineer vs GPU Developer?

AspectCuda EngineerGPU Developer
Required CredentialsBachelor's or Master's in Computer Science, Engineering, or related; knowledge of CUDA, C++, parallel programmingBachelor's or Master's in Computer Science, Engineering, or related; experience with GPU programming, CUDA, OpenCL
Work EnvironmentResearch labs, tech companies, hardware firms focusing on GPU accelerationSoftware development teams, gaming, AI, scientific computing sectors
Employer & Industry UsageHardware manufacturers, AI companies, high-performance computing firmsGame development, scientific research, machine learning applications

While both roles involve GPU programming and CUDA expertise, a Cuda Engineer primarily focuses on developing and optimizing CUDA-based solutions for hardware acceleration. In contrast, a GPU Developer works on broader GPU programming tasks, including application development across various platforms. The roles often overlap but differ in scope and specific focus areas.

How much do Cuda engineers make?

Cuda engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in GPU programming and parallel computing tend to have higher salaries. Certifications and a strong understanding of CUDA tools can also influence compensation.

What are some common challenges faced by CUDA engineers when optimizing GPU-accelerated applications?

CUDA Engineers frequently encounter challenges such as managing memory effectively between the host and the device, optimizing kernel performance, and minimizing data transfer bottlenecks. Debugging parallel code can also be complex due to race conditions and the difficulty of reproducing timing-related bugs. Collaborating closely with software developers and data scientists is essential to ensure that GPU resources are leveraged efficiently and that the application's overall performance meets project goals.

What are the key skills and qualifications needed to thrive as a CUDA engineer?

To thrive as a CUDA Engineer, you need a strong proficiency in C/C++ programming, parallel computing concepts, and deep knowledge of GPU architectures, often supported by a computer science or engineering degree. Experience with NVIDIA CUDA Toolkit, profiling/debugging tools, and sometimes certifications like NVIDIA DLI are highly valuable. Strong problem-solving, attention to detail, and effective communication skills help you optimize code and collaborate across teams. These skills ensure efficient development of high-performance GPU applications and successful project delivery in compute-intensive fields.

Are CUDA engineers in demand?

CUDA engineers are in high demand due to the increasing use of GPU computing in fields like artificial intelligence, machine learning, and high-performance computing. Skills in CUDA programming, parallel processing, and related tools are highly valued by employers across technology, research, and industry sectors.
What are popular job titles related to Cuda Engineer jobs in Raleigh, NC? For Cuda Engineer jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Cuda Engineer jobs in Raleigh, NC look for? The top searched job categories for Cuda Engineer jobs in Raleigh, NC are:
Infographic showing various Cuda Engineer job openings in Raleigh, NC as of July 2026, with employment types broken down into 100% Full Time. Highlights an 71% In-person, and 29% Remote job distribution, with an average salary of $104,286 per year, or $50.1 per hour.

Senior System Software Engineer - Data Center Compute Diagnostics

Nvidia

Durham, NC

$118K - $156K/yr

Full-time

Posted 5 days ago


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies


Job description

We are seeking a senior system software engineer to lead technically and own key parts of our low-level diagnostic software. This software supports next-generation data center GPUs and rack-scale AI systems. Our team builds software that exercises and validates complex hardware, including processing units, storage and cache architectures, NICs, PCIe and NVLink interfaces, power delivery, and thermal behavior. This role is well suited to a senior embedded, firmware, device-driver, hardware-validation, or systems software engineer with a record of leading complex software projects that directly interface with hardware. Relevant experience may come from GPUs, CPUs, networking, storage, servers, embedded systems, or other complex silicon-based products. Prior GPU, CUDA, or GEMM experience is helpful for the role; deep experience developing low-level software for complex hardware systems is required.

This is a hands-on software development role in which you will architect, implement, debug, and maintain key components of the diagnostic software through validation, productization, and field support. In addition to making substantial individual code contributions, you will lead complex development efforts, mentor other engineers, and collaborate with hardware architects, driver developers, silicon-validation engineers, manufacturing teams, and field engineers to bring up new hardware and diagnose difficult system failures. Join an exciting, rewarding, and fast-moving environment building the systems that power modern AI.

What you'll be doing:

  • Architecting and developing diagnostic and stress software in C/C++ and Python for complex hardware systems.

  • Leading development efforts across multiple engineers, breaking ambiguous problems into actionable work, and mentoring engineers in low-level software development and debugging.

  • Interfacing with hardware blocks, firmware, Linux device drivers, registers, telemetry, and low-level debugging tools.

  • Assessing new hardware features and defining effective diagnostic and stress strategies for engineering validation, manufacturing, product qualification, and field use.

  • Designing targeted tests for compute engines, memory and cache subsystems, DMA engines, NICs, PCIe/NVLink interfaces, power, and thermal behavior.

  • Developing diagnostic and stress workloads ranging from low-level tests for GPU hardware to higher-level AI workloads using CUDA programming, GEMM-style compute, NCCL, and PyTorch.

  • Investigating complex hardware and software failures involving memory errors, ECC, data integrity, performance, thermals, voltage/frequency behavior, and high-speed interfaces.

  • Using modern development and analysis tools, including AI-assisted tools where appropriate, to accelerate coding, debugging, test creation, and failure analysis.

What we need to see:

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

  • 12+ years of experience in embedded software, firmware, Linux device drivers, systems software, hardware validation, diagnostics, or silicon bring-up.

  • Experience providing technical leadership for a complex software component or project, including coordinating work among various engineers and mentoring others.

  • Strong programming skills in C and C++, plus working proficiency in Python.

  • Extensive experience developing software that interacts with hardware, firmware, device drivers, hardware registers, or other low-level interfaces.

  • Background with PCIe, NVLink, or networking technologies such as Ethernet or InfiniBand.

  • Strong understanding of computer architecture concepts such as memory systems, caches, interrupts, DMA, buses, device I/O, bandwidth constraints, and hardware error behavior.

  • Experience debugging complex failures across hardware, firmware, device drivers, operating systems, and applications.

  • Ability to define technical direction, make sound engineering tradeoffs, and drive ambiguous problems through completion across organizational boundaries.

  • Excellent written and verbal communication skills, including the ability to communicate effectively with hardware architects, software engineers, manufacturing teams, field engineers, and technical leadership.

Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

    Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.

    You will also be eligible for equity and benefits.

    Applications for this job will be accepted at least until August 3, 2026.

    This posting is for an existing vacancy.

    NVIDIA uses AI tools in its recruiting processes.

    NVIDIA is committed to fostering an inclusive 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.

    What Nvidia employees say

    Pay

    Benefits

    Hours and flexibility

    Workplace

    Get the full story on Breakroom


    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