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Cuda Kernel Engineer Jobs in Oklahoma (NOW HIRING)

Senior Software Engineer Specialist

Tulsa, OK · On-site

$92K - $121K/yr

Develop and maintain software in CUDA C/C++ for NVIDIA GPU-based systems. Translate and optimize ... Improve software performance through optimization of kernel execution, memory usage, and data ...

Cuda Kernel Engineer information

What is a CUDA Kernel Engineer?

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 skills and qualifications are needed to be a CUDA Kernel Engineer?

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.

What are 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.

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Senior Software Engineer Specialist

L3HHCM20

Tulsa, OK • On-site

$92K - $121K/yr

Full-time

Posted 17 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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