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Cuda Engineer Jobs in Wisconsin (NOW HIRING)

ConfigUSA

CUDA Programmer

Waukesha, WI · On-site

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 ...

NVIDIA Corporation

WI · On-site

$184 - $356.50/hr

Join the NVIDIA Developer Tools team and empower engineers worldwide developing groundbreaking ... CUDA/OpenCL knowledge. * Experience with code patching. * ELF/DWARF knowledge. Compensation ...

New

Jobgether

WI · On-site

$120 - $180/hr

Experience with GPU performance optimization, including technologies such as CUDA, ROCm, Triton, or ... Strong programming skills and the ability to work across machine learning and systems engineering ...

New

Bitdeer Group

WI · On-site

$100 - $130/hr

Degree in Computer Science, Data Engineering, or related technical field (Master's preferred ... Familiarity with the NVIDIA infrastructure stack (CUDA, NCCL, Triton Inference Server) * Deep ...

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Cuda Engineer information

See Wisconsin salary details

$36.8K

$108.3K

$138.8K

How much do cuda engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for cuda engineer in Wisconsin is $108,285.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,300.00 and $137,300.00 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 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.

How much do Cuda engineers make?

Cuda engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in GPU programming and parallel computing tend to have higher salaries. Certifications and a strong understanding of CUDA tools can also influence compensation.

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 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.

Are CUDA engineers in demand?

CUDA engineers are in high demand due to the increasing use of GPU computing in fields like artificial intelligence, machine learning, and high-performance computing. Skills in CUDA programming, parallel processing, and related tools are highly valued by employers across technology, research, and industry sectors.
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What cities in Wisconsin are hiring for Cuda Engineer jobs? Cities in Wisconsin with the most Cuda Engineer job openings:
Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in Wisconsin as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $108,285 per year, or $52.1 per hour.

CUDA Programmer

ConfigUSA

Waukesha, WI • On-site

Contractor

Re-posted 13 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.