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Gpu Programming Jobs in Raleigh, NC (NOW HIRING)

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

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Data Engineer

Durham, NC · On-site

$57 - $63/hr

Develop and test protocols for GPU-accelerated execution of R- and Python-based models. * Create ... Strong experience using the R programming language. * Experience designing and maintaining database ...

Nvidia

Senior Developer Technology Engineer - AI

Durham, NC · Hybrid

$52.75 - $69.50/hr

... programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, etc. * Hands on experience doing low-level performance optimizations. * In-depth expertise with CPU and GPU architecture fundamentals. * Good ...

Nvidia

EDA Workflow Optimization Engineer

Durham, NC · Hybrid

$107K - $127K/yr

Hands-on experience running GPU-based workloads in a batch computing environment and a deep understanding of distributed system principles. * Strong programming and debugging skills with C/C ...

Nvidia

Senior ASIC Power Engineer

Durham, NC

Develop innovative HW, GPU and system designs to extend the state of the art performance and efficiency * You are expected to understand the design and implementation, develop power metrics and drive ...

Teledyne Technologies

Software Engineer

Durham, NC

Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ... S. in Computer Science, Applied Mathematics, Computer Engineering or Electrical Engineering ...

Teledyne FLIR LLC

Software Engineer

Durham, NC · On-site

Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ... S. in Computer Science, Applied Mathematics, Computer Engineering or Electrical Engineering ...

Teledyne

Software Engineer

Durham, NC

Knowledge of computer architecture (GPU/FPGA/distributed computing), operating systems, networking ... S. in Computer Science, Applied Mathematics, Computer Engineering or Electrical Engineering ...

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Gpu Programming information

See Raleigh, NC salary details

$32.1K

$63.2K

$92.8K

How much do gpu programming jobs pay per year?

As of Jun 21, 2026, the average yearly pay for gpu programming in Raleigh, NC is $63,160.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,100.00 and $77,800.00 per year, depending on experience, location, and employer.

What is a GPU Programming job?

A GPU Programming job involves writing and optimizing code to run on Graphics Processing Units (GPUs) for parallel computing tasks. This role is commonly found in fields like machine learning, scientific computing, gaming, and data analytics. GPU programmers use languages such as CUDA, OpenCL, or Vulkan to accelerate computations and improve performance. They work closely with software engineers and data scientists to optimize algorithms for high-performance applications.

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

To excel in GPU Programming, you need a strong background in parallel computing concepts, mathematics, and proficiency in languages such as CUDA, OpenCL, or DirectX/OpenGL, often supported by a degree in computer science, engineering, or a related field. Familiarity with NVIDIA and AMD GPU development tools, performance profilers, and possibly certifications like NVIDIA's Deep Learning Institute courses are valuable. Teamwork, effective communication, and strong problem-solving abilities are essential soft skills in this field. These competencies enable efficient development, optimization, and integration of high-performance GPU code in real-world applications.

What types of projects or applications do GPU Programmers commonly work on?

GPU Programmers are often involved in developing or optimizing software for high-performance applications such as machine learning, scientific simulations, real-time rendering in gaming and visualization, and video/image processing tools. Their daily work may include collaborating with software engineers, data scientists, and hardware teams to create efficient, scalable parallel algorithms that leverage GPU capabilities. The role frequently requires problem-solving to maximize computational efficiency and troubleshooting complex performance bottlenecks. By working across multidisciplinary teams, GPU Programmers help deliver robust solutions for data-intensive problems in areas like healthcare, finance, automotive technology, and entertainment.

What are popular job titles related to Gpu Programming jobs in Raleigh, NC? For Gpu Programming jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Gpu Programming jobs in Raleigh, NC look for? The top searched job categories for Gpu Programming jobs in Raleigh, NC are:
Gpu Programming jobs near you
Infographic showing various Gpu Programming job openings in Raleigh, NC as of June 2026, with employment types broken down into 1% As Needed, 97% Full Time, 1% Temporary, and 1% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $63,160 per year, or $30.4 per hour.

Kubernetes Platform Engineer - AI Infrastructure

Webex Events (formerly Socio)

Durham, NC • On-site

$97K - $127K/yr

Other

Posted 6 days ago

Job description

Kubernetes Platform Engineer - Ai Infrastructure - Hybrid

Join our Platform Engineering team to design, build, and operate large-scale, on-prem Kubernetes infrastructure powering next-generation AI/ML platforms, including GPU-enabled environments for traditional models and LLMs. You will lead the technical direction of scalable, reliable systems, managing the Kubernetes control plane and extending platform capabilities through custom controllers and operators. You'll architect ML platforms, implement Infrastructure as Code with Golang, and drive MLOps best practices. Partnering closely with data scientists and ML engineers, you'll enable high-performance AI workloads while leveraging AIOps for automation and reliability. This role requires strong hands-on on-prem Kubernetes experience and offers opportunities to mentor engineers and influence platform strategy in a hybrid environment.

Your Impact / Responsibilities as a Kubernetes Platform Engineer, you will:

  • Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos), with ownership of control plane, etc, and cluster lifecycle.
  • Architect scalable, multi-tenant platform infrastructure as the foundation for AI/ML and GenAI workloads.
  • Enable and optimize AI/ML workloads, including GPU-based environments for training, inference, and model deployment.
  • Partner with data scientists and ML engineers to onboard and scale ML pipelines and workflows.
  • Build platform capabilities using Kubernetes controllers, operators, CRDs, and Golang/Python services.
  • Implement Infrastructure as Code, automation, and AIOps-driven self-healing using platform telemetry and observability.
  • Ensure reliability through performance tuning (scheduling, resource utilization) and participate in on-call support and incident response.
Minimum Qualifications
  • 5+ years of software engineering experience, including supporting AI/ML or GPU-based workloads on Kubernetes platforms
  • 3+ years operating Kubernetes in production with control plane ownership, preferably in on-prem or self-managed environments
  • Strong experience with etcd management (backup, restore, recovery) and Kubernetes cluster upgrades
  • Proficiency in Go with experience building Kubernetes controllers/operators, CRDs, and webhooks
  • Deep understanding of Kubernetes internals (API server, scheduler, controller loops, reconciliation patterns)
  • Proven ability to debug and operate large-scale distributed systems in production environments, including participation in on-call rotations
Preferred Qualifications
  • Experience with bare-metal or on-prem infrastructure at scale
  • Experience enabling or supporting GPU-based workloads in Kubernetes environments
  • Familiarity with AI/ML platforms, pipelines, or tooling (e.g., model training, inference, or orchestration)
  • Experience building internal developer platforms or platform-as-a-service (PaaS) capabilities
  • Exposure to AIOps, including automation, anomaly detection, or self-healing systems
  • Experience applying statistical or ML techniques to operational data for reliability, performance, or capacity planning

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