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Cuda Engineer Jobs in Oklahoma (NOW HIRING)

L3HHCM20

Senior Software Engineer Specialist

Tulsa, OK · On-site

$92K - $121K/yr

Real-time Development Engineer 4 Job Code : 41937 Job Location: Tulsa, OK Schedule: 9/80 employees ... Develop and maintain software in CUDA C/C++ for NVIDIA GPU-based systems. Translate and optimize ...

Accenture

$93K - $123K/yr

Architect and deploy with NVIDIA platform tools including Base Command Manager (BCM), NGC, NCCL, NVLink, and CUDA along with LLM inference engines (TensorRT-LLM), production serving frameworks (vLLM ...

Accenture

AI & HPC Infrastructure Engineer

Oklahoma City, OK · On-site

$99K - $131K/yr

Architect and deploy with NVIDIA platform tools including Base Command Manager (BCM), NGC, NCCL, NVLink, and CUDA along with LLM inference engines (TensorRT-LLM), production serving frameworks (vLLM ...

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Cuda Engineer information

See Oklahoma salary details

$33.7K

$99.1K

$127K

How much do cuda engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for cuda engineer in Oklahoma is $99,057.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,700.00 and $125,600.00 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 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.

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 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 popular job titles related to Cuda Engineer jobs in Oklahoma?

For Cuda Engineer jobs in Oklahoma, the most frequently searched job titles are:

What job categories do people searching Cuda Engineer jobs in Oklahoma look for?

The top searched job categories for Cuda Engineer jobs in Oklahoma are:

What cities in Oklahoma are hiring for Cuda Engineer jobs?

Cities in Oklahoma with the most Cuda Engineer job openings:

Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in Oklahoma as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, and 4% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $99,057 per year, or $47.6 per hour.

Senior Software Engineer Specialist

L3HHCM20

Tulsa, OK • On-site

$92K - $121K/yr

Full-time

Posted 14 days ago

Job description

Job Title: Real-time Development Engineer 4

Job Code : 41937

Job Location: Tulsa, OK

Schedule: 9/80 employees work 9 out of 14 days- totaling 80 hours worked- and have every other Friday off

 

Job Description:


L3Harris is seeking a Developer, Software Engineering with experience in CUDA, C/C++, and Python to support the development of high-performance GPU-accelerated software for advanced mission applications. The selected candidate will work closely with software and algorithm teams to translate computational and image-processing algorithms into efficient CUDA C/C++ implementations for execution on NVIDIA GPU platforms.

This role is ideal for an engineer who enjoys performance optimization, parallel programming, and the challenge of converting algorithmic concepts into production-ready software. The position includes design, implementation, integration, profiling, test, and optimization of GPU-based software in a Linux development environment.  Our development efforts aim to achieve the best possible performance to meet demanding real-time requirements.

Essential Functions:

       Develop and maintain software in CUDA C/C++ for NVIDIA GPU-based systems.

       Translate and optimize computational algorithms into efficient GPU implementations.

       Support development of image processing and related high-performance software applications.

       Analyze existing software and algorithms to identify opportunities for parallelization and acceleration.

       Improve software performance through optimization of kernel execution, memory usage, and data movement.

       Develop and execute unit, integration, and performance tests to validate functionality and accuracy.

       Collaborate with cross-functional teams including software, systems, and algorithm engineers.

       Participate in software design reviews, code reviews, and technical discussions.

       Support software integration and debugging in Linux-based environments.

       Document software design, implementation, test results, and performance findings.

Qualifications:

 

       Bachelor's Degree and a minimum of 6 years of prior relevant software experience. Graduate Degree and a minimum of 4 years of prior related experience. In lieu of a degree, minimum of 10 years of prior related experience.

       Minimum 6 years of experience with C/C++.

       Minimum 2 years of experience with Python.

       Minimum 4 years of experience with NVIDIA CUDA.

       Experience developing software in Linux environments.

       Experience implementing or optimizing algorithms for performance-sensitive applications.

       Experience using source control and standard software development practices.

       Must be eligible to obtain a U.S. security clearance.

Preferred Additional Skills:

       Experience with GPU profiling and performance analysis tools such as Nsight Systems, Nsight Compute, NVIDIA Visual Profiler, Compute Sanitizer.

       Experience with image processing and/or computer vision applications. 

       Experience with TensorRT, cuFFT, cuBLAS, cuDNN, NPP, Thrust, and CUB. 

       Experience with Containerization with technologies such as Docker is a plus.

       Experience using DevOps pipelines and tooling is a plus.

       Experience translating prototype or research algorithms into production-quality implementations.

       Familiarity with software testing, benchmarking, and debugging techniques in high-performance computing environments.

       Experience developing for multiple CUDA Architectures and Compute Capabilities

       Knowledge of GPU Direct/RDMA, NVIDIA containerization, machine parallelism

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