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Cuda Programming Jobs (NOW HIRING)

ConfigUSA

CUDA Programmer

Waukesha, WI · On-site

Work with CPU-GPU parallel programming models and optimize data transfer. * Leverage NVIDIA libraries (CUDA, cuBLAS, cuDNN, NCCL as applicable). * Collaborate with system, compute, or AI/ML teams to ...

Dalcom LLC

C++/CUDA-Signal Processing Engineer

Aberdeen, MD · On-site

$140K - $176K/yr

... CUDA programming, to support Science and Technology (S&T) efforts for radar and signal systems at Aberdeen Proving Ground. About Us: Founded in 1998, Dalcom Engineering is a HUBZone certified small ...

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How much do cuda programming jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for cuda programming in the United States is $54.36, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $63.46 per hour, depending on experience, location, and employer.

Are CUDA programmers in demand?

CUDA programmers are in high demand due to the growing need for high-performance computing in fields like artificial intelligence, scientific research, and data analysis. Skills in parallel programming, GPU architecture, and CUDA toolkit are highly valued by employers across technology, automotive, and research industries.

What is the difference between Cuda Programming vs GPU Developer?

AspectCuda ProgrammingGPU Developer
Required CredentialsKnowledge of CUDA, C/C++, parallel computingKnowledge of GPU architecture, CUDA, OpenCL, C/C++
Work EnvironmentHigh-performance computing, scientific research, AIGraphics, gaming, scientific visualization, AI
Industry UsageTech companies, research labs, AI firmsGaming, entertainment, tech, research

While Cuda Programming focuses specifically on writing code using NVIDIA's CUDA platform for parallel processing, GPU Developers have a broader role that includes designing, optimizing, and implementing GPU-based solutions across various platforms and technologies. Both roles require knowledge of GPU architecture and programming languages like C/C++, but GPU Developers often work on a wider range of applications beyond CUDA-specific projects.

What does a CUDA programmer do?

A CUDA programmer develops software that leverages NVIDIA's CUDA platform to perform parallel computing tasks on GPUs. They write and optimize code using languages like C++ and CUDA-specific libraries to accelerate applications in fields such as scientific computing, machine learning, and graphics processing.
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Cuda Programming jobs near you
Infographic showing various Cuda Programming job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 79% Full Time, 14% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $113,061 per year, or $54.4 per hour.

CUDA Programmer

ConfigUSA

Waukesha, WI • On-site

Contractor

Re-posted 14 days ago

Job description

CUDA Programmer
Location: Waukesha, WI

We are seeking a skilled CUDA Programmer to design, develop, and optimize high-performance applications on NVIDIA GPUs. The role focuses on accelerating compute-intensive workloads, optimizing memory usage, and collaborating with system and application teams to maximize GPU performance.

Key Responsibilities

  • Profile and tune GPU applications for performance, memory efficiency, and scalability.
  • Work with CPU–GPU parallel programming models and optimize data transfer.
  • Leverage NVIDIA libraries (CUDA, cuBLAS, cuDNN, NCCL as applicable).
  • Collaborate with system, compute, or AI/ML teams to integrate GPU-accelerated components.
  • Debug GPU kernels and address performance bottlenecks using NVIDIA profiling tools.
  • Ensure portability and performance across different NVIDIA GPU architectures.

Required Skills

  • Strong experience in CUDA programming and parallel computing concepts.
  • In-depth understanding of NVIDIA GPU architecture (threads, warps, SMs, memory hierarchy).
  • Proficiency in C/C++ for high-performance computing.
  • Experience with CUDA profiling and debugging tools (Nsight, nvprof).
  • Solid understanding of multi-threading, memory optimization, and performance tuning.

Preferred Skills

  • Experience with AI/ML, HPC, or graphics workloads on GPUs.
  • Familiarity with multi-GPU programming and communication frameworks (NCCL, MPI).
  • Exposure to Python bindings (CUDA Python, PyTorch extensions).
  • Experience with Linux-based development environments.