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Cuda Engineer Jobs in Raleigh, NC (NOW HIRING)

SAS

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 ...

SAS

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 ...

Nvidia

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 ...

Danaher

Senior Software Engineer - USA Remote

Raleigh, NC · Remote

$119K - $157K/yr

GPU programming using CUDA; Targeting ARM & X86 processing environments; User Interface design; Database engines - SQLite, MongoDB, DynamoDB; Moq Unit testing framework * Protocols Experience: MQTT;

Danaher

Senior Software Engineer - USA Remote

Durham, NC · Remote

$118K - $156K/yr

GPU programming using CUDA; Targeting ARM & X86 processing environments; User Interface design; Database engines - SQLite, MongoDB, DynamoDB; Moq Unit testing framework * Protocols Experience: MQTT;

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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 Jul 14, 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 are CUDA Engineers?

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.

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, and why are they important?

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.
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:
Cuda Engineer jobs near you
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 Software Engineer, CUTLASS Kernels

Senior Software Engineer, CUTLASS Kernels

Nvidia

Durham, NC

$118K - $156K/yr

Full-time

Posted 12 days ago

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

NVIDIA's high-performance computing platforms are powering the AI revolution across many applications and industries. Within our software stack, CUTLASS stands out as a popular open-source ecosystem dedicated to high-performance linear algebra and Tensor Core primitives. Since 2017, it has provided the community with C++ and Python abstractions to implement custom matrix multiply (GEMM) and related math and deep learning computations on NVIDIA GPUs.

If you are passionate about developing and optimizing math kernels to extract the highest performance out of the hardware architecture, apply to join the CUTLASS team today!

What you'll get to do:

  • Write Tensor Core-based deep learning kernels such as grouped-GEMM, attention, and convolution using CUTLASS CUDA C++ and Python DSL for Blackwell, Rubin, and future architectures.

  • Optimize kernels for peak throughput on both silicon and software performance simulators.

  • Collaborate with teams across NVIDIA including the GPU architecture, NVVM/PTX compiler, CUDA library, and DL frameworks teams to ensure fast, functional, and timely kernel delivery to customers.

What we need to see:

  • Masters or PhD degree in Computer Science, Computer Engineering, or related field (or equivalent experience).

  • 3+ years of relevant industry experience.

  • Strong proficiency in C++ programming and software design, including debugging, performance evaluation, and testing.

  • Experience with CUDA, OpenCL, HIP, SYCL, Mojo, Pallas, Triton, Mosaic, Halide, or any general-purpose or domain-specific programming language targeting highly parallel accelerators.

  • Deep understanding of computer architecture and some experience working at the assembly level.

Ways to stand out from the crowd:

  • Experience writing code specifically targeting NVIDIA Tensor Cores, particularly through PTX or CUDA/cuTile.

  • Open-source contributions to math kernel libraries or frameworks.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hard working people in the world working for us. If you're creative, autonomous, and love a challenge, consider joining our Deep Learning Library team and help us build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.

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 for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 5, 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

Hours and flexibility

Workplace

Get the full story on Breakroom

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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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