Senior Software Engineer Specialist

L3Harris is dedicated to recruiting and developing high-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers’ mission and quest for professional growth. L3Harris provides an inclusive, engaging environment designed to empower employees and promote work-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do.

L3Harris is the Trusted Disruptor in defense tech. With customers’ mission-critical needs always in mind, our employees deliver end-to-end technology solutions connecting the space, air, land, sea and cyber domains in the interest of national security.

Job Location: Tulsa, OK

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

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.

• · 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.

• · 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.

• · 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 pr oduction-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