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Cuda Software Engineer Jobs (NOW HIRING)

Nvidia

OR · On-site

NVIDIA's Quantum Computing team is searching for an outstanding software engineer to build the toolchain of the future. Join us in developing the CUDA-Q platform for programming powerful hybrid ...

DeepSig Inc Careers Page

Senior Software Engineer

Arlington, VA · On-site

$141K - $185K/yr

We are seeking a Senior Software Engineer to expand that portfolio utilizing the latest technology ... CUDA) * Software-defined radio (SDR) experience * Embedded system experience * This opportunity is ...

DeepSig Inc

Senior Software Engineer

Arlington, VA · On-site +1

$141K - $185K/yr

We are seeking a Senior Software Engineer to expand that portfolio utilizing the latest technology ... CUDA) * Software-defined radio (SDR) experience * Embedded system experience * This opportunity is ...

Nvidia

OR · On-site

NVIDIA's Quantum Computing team is searching for an outstanding software engineer to build the toolchain of the future. Join us in developing the CUDA-Q platform for programming powerful hybrid ...

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Cuda Software Engineer information

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How much do cuda software engineer jobs pay per year?

As of Jun 4, 2026, the average yearly pay for cuda software engineer in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

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

To thrive as a CUDA Software Engineer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid understanding of GPU architectures, typically supported by a computer science or related degree. Familiarity with NVIDIA CUDA Toolkit, GPU debugging/profiling tools, and experience with performance optimization are essential. Analytical thinking, problem-solving, and effective teamwork skills help you tackle complex computational challenges and collaborate on large-scale projects. These skills are crucial to efficiently develop high-performance GPU-accelerated applications and deliver optimized solutions in demanding technical environments.

What are some common challenges Cuda Software Engineers face when optimizing code for GPU performance?

Cuda Software Engineers often encounter challenges related to memory management, such as minimizing data transfers between CPU and GPU and optimizing memory access patterns to avoid bottlenecks. Additionally, ensuring code scalability across different GPU architectures and achieving efficient parallelization can be complex. Collaborating closely with data scientists, hardware engineers, and other developers is essential to troubleshoot performance issues and maximize throughput in real-world applications.

What are CUDA Software Engineers?

CUDA Software Engineers are specialists who develop software using NVIDIA's CUDA (Compute Unified Device Architecture) platform to leverage the parallel processing power of GPUs. They optimize algorithms and applications for high performance on CUDA-enabled devices, often in fields like scientific computing, machine learning, and graphics. Their work involves writing and debugging code in languages such as C, C++, or Python with CUDA extensions, and collaborating with teams to ensure efficient execution of compute-intensive tasks.

Software Engineer - Software Engineer - GPU, C++, OpenCL, CUDA

Hudson Manpower

Waukesha, WI • On-site

Contractor

Posted 29 days ago

Job description

Position: Software Engineer - GPU, C++, OpenCL, CUDA
Location: Waukesha, WI (Onsite)
Exp: 5 - 9 yrs
Key Skills: GPU, C++, OpenCL, CUDA, OneAPI, Matlab
Only USC / GC
Job Requirements
The CT Program is working on upgrading CT scanners used worldwide. The center is currently concentrating on the ongoing enhancement of the next generation of CT machines, including their essential workflows and applications. For that purpose, proficient and experienced resources are required.
Primary Objective:
  1. Leverage proprietary software platform to implement image processing algorithms on GPUs. (C++/OpenCL/CUDA/OneAPI)
  2. Improve image chain performance using heterogeneous high-performance computing (HPC) to meet customer expectations
  3. Ensure quality and compliance of productized code per regulatory expectations

Detailed Requirements:
  1. Productized CT image processing algorithms on GPU, including ported algorithms from Matlab to GPU, or OpenCL to CUDA
  2. Improved image chain & algorithm performance compared to initial benchmarks
  3. Perform GPU profiling, identify algorithm bottlenecks, troubleshoot and resolve performance issues
  4. Improve GPU utilization leveraging heterogenous HPC knowledge.
  5. Perform testing, reliability analysis, performance benchmarks and document results
  6. Execute test procedures with high quality and rigor, following Good Documentation Practices

Work Experience
Skills:
  1. Programming Languages: C++, OpenCL, CUDA, OneAPI
  2. Image Processing Algorithms: Implementation and optimization on GPUs
  3. High-Performance Computing (HPC): Knowledge of heterogeneous HPC
  4. Profiling and Performance Analysis: GPU profiling, identifying bottlenecks, troubleshooting, and resolving performance issues
  5. Testing and Documentation: Performing testing, reliability analysis, performance benchmarks, and documenting results following Good Documentation Practices

Additional Experience:
Productizing Algorithms: Experience in productizing CT image processing algorithms on GPU
Porting Algorithms: Experience in porting algorithms from Matlab to GPU or OpenCL to CUDA
Improving Performance: Proven track record of improving image chain and algorithm performance compared to initial benchmarks
Quality and Compliance: Ensuring quality and compliance of productized code per regulatory expectations
Best regards,
Prasad Kalsekar | Hudson Manpower
Email: prasad@hudsonmanpower.com

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