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Cuda Kernel Engineer Jobs in Raleigh, NC (NOW HIRING)

As leading developers and maintainers of the vLLM project, and inventors of state-of-the-art ... CUDA kernel compilation to Kubernetes-orchestrated model serving on OpenShift. If you want to work ...

... CUDA library, and DL frameworks teams to ensure fast, functional, and timely kernel delivery to ... Strong proficiency in C++ programming and software design, including debugging, performance ...

Support CPU architects and performance engineers in their use of functional models, performance ... Experience with Linux kernel bringup and debug * Familiarity with CUDA * Experience with CPU/GPU ...

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Cuda Kernel Engineer information

What are some common challenges faced by Cuda Kernel Engineers when optimizing GPU code for performance?

Cuda Kernel Engineers often encounter challenges such as managing memory hierarchy efficiently, minimizing data transfer between host and device, and avoiding thread divergence. Ensuring optimal occupancy and maximizing parallelism while preventing bottlenecks like bank conflicts or uncoalesced memory access are also key concerns. Collaborating closely with software architects and data scientists is common, as solutions frequently require balancing algorithmic accuracy with hardware limitations. Addressing these challenges requires continuous profiling, testing, and iterative optimization.

What are Cuda Kernel Engineers?

Cuda Kernel Engineers are specialized software developers who design, implement, and optimize parallel computing algorithms using NVIDIA's CUDA platform. They write 'kernels,' which are functions that run on Graphics Processing Units (GPUs) to accelerate computational tasks in areas such as machine learning, scientific simulations, and graphics rendering. These engineers need strong skills in C/C++ programming, GPU architecture, and performance optimization techniques. Their work is crucial for applications that require high-speed data processing and efficient resource utilization.

What are the key skills and qualifications needed to thrive as a CUDA Kernel Engineer, and why are they important?

To thrive as a CUDA Kernel Engineer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid foundation in GPU architectures, typically supported by a degree in computer science or a related field. Expertise in NVIDIA CUDA toolkits, GPU profiling tools like Nsight, and familiarity with version control systems are essential. Analytical thinking, problem-solving abilities, and effective collaboration skills help engineers optimize code and work well within development teams. These skills and qualities are crucial for delivering high-performance, scalable GPU solutions in computationally intensive applications.
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Infographic showing various Cuda Kernel Engineer job openings in Raleigh, NC as of June 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution.
System Software Engineer - Data Center GPU Compute Diagnostics

System Software Engineer - Data Center GPU Compute Diagnostics

Nvidia Corporation

Durham, NC • On-site

$167K - $198K/yr

Full-time

Posted 4 hours 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/NVLink interfaces, 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, NICs or high-speed interconnects such as PCIe.
Good interpersonal skills are required as this role will involve close collaboration with hardware architecture, silicon validation, manufacturing and 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 efficiently validate and test next-generation processors and systems. Join an exciting, rewarding and fast paced environment!
What you'll be doing:
  • Working closely with hardware architecture, driver, manufacturing, and field teams through the product development lifecycle of rack-scale AI systems.
  • Implementing and maintaining CUDA/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 with additional load in NVLink, PCIe or CPU subsystems.
  • Contributing to higher-level AI workload tests, including PyTorch-based large model workloads that stress GPUs, memory, interconnects, thermals, and system software under realistic rack-scale AI use cases.
  • Bringing up and validating new 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/NVLink errors.

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, ECC behavior and DMA engines.
  • Familiarity with GEMM-style workloads.
  • Awareness of voltage/frequency characterization, thermal testing, power stress, or related silicon validation concepts such as Vmin/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