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How much do cuda gpu software engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for cuda gpu 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 is a CUDA GPU software engineer?

CUDA GPU Software Engineers are specialized software developers who design, implement, and optimize applications that run on NVIDIA GPUs using the CUDA (Compute Unified Device Architecture) programming platform. They leverage parallel computing capabilities of GPUs to accelerate computational tasks, often working in fields such as artificial intelligence, scientific computing, and graphics processing. Their responsibilities typically include writing CUDA C/C++ code, optimizing algorithms for GPU execution, debugging performance bottlenecks, and collaborating with other engineers to integrate GPU-accelerated solutions into larger systems.

What are some typical challenges faced by a CUDA GPU software engineer when optimizing code for parallel execution?

CUDA GPU Software Engineers often encounter challenges such as identifying sections of code that can be efficiently parallelized, managing memory bandwidth, and minimizing latency due to data transfers between CPU and GPU. Debugging and profiling GPU kernels can also be more complex than on traditional CPUs, requiring specialized tools. Additionally, engineers must ensure their code scales well across different GPU architectures, which can involve understanding hardware-specific constraints and optimizing for both speed and resource usage.

What are the key skills and qualifications needed to thrive as a CUDA GPU software engineer, and why are they important?

To thrive as a CUDA GPU Software Engineer, you need strong programming skills in C/C++, a deep understanding of parallel computing concepts, and experience with GPU architectures, typically backed by a degree in computer science, engineering, or a related field. Proficiency in CUDA, profiling/debugging tools like NVIDIA Nsight, and familiarity with libraries such as cuBLAS or cuDNN are commonly required. Analytical thinking, problem-solving abilities, and effective teamwork are standout soft skills for this role. These skills ensure efficient development of high-performance GPU-accelerated applications, enabling breakthroughs in areas like scientific computing, AI, and graphics.

What are popular job titles related to Cuda Gpu Software Engineer jobs?

For Cuda Gpu Software Engineer jobs, the most frequently searched job titles are:

Infographic showing various Cuda Gpu Software Engineer job openings in the United States as of June 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 90% Physical, 4% Hybrid, and 6% Remote job distribution, with an average salary of $147,524 per year, or $70.9 per hour.

GPU Software Engineer/GPU Architect

San Jose, CA • On-site

Triune Infomatics Inc
IT Services • 51 - 200 employees
Learn more about Triune Infomatics Incexternal

$164K - $202K/yr

Other

Re-posted 17 days ago

Job description

Role: GPU Software Engineer/GPU Architect
Location: San Jose, CA (Remote/Hybrid)
Duration: Long-term >> ongoing contract
 
Overview: We''re looking for a strong GPU Software Engineer/GPU Architect to join a highimpact engineering team working on nextgeneration AI, GPU, and semiconductor technologies. This role focuses on GPU kernel development, memory architecture, and integration with modern inference systems such as vLLM and SGLang. You''ll work onsite in San Jose, collaborating closely with a team of engineers building highperformance GPUaccelerated systems.
  • Develop and optimize CUDA/ROCm kernels for AI workloads
  • Work with HBM, memory hierarchy, thread scheduling, and P2P communication
  • Integrate GPU kernels with vLLM, SGLang, and other inference servers
  • Build highperformance components in C++ and Python
  • Support AI frameworks such as PyTorch and TensorFlow
  • Optimize multiGPU scaling, KVcache, and attention kernels
  • Profile and debug GPU workloads using Nsight, rocprof, etc.
  • Collaborate with crossfunctional GPU, AI, and semiconductor teams
Required Skills:
  • Strong experience with CUDA, ROCm/HIP, OpenCL, or MPI
  • Deep understanding of GPU architecture, HBM, memory models, and thread hierarchies
  • Handson experience with AMD/NVIDIA GPU software stacks
  • Expertlevel C++ and Python
  • Experience with PyTorch or TensorFlow
  • Experience with vLLM, SGLang, or similar inference systems
Preferred Skills:
  • RDMA, RoCE, InfiniBand, or Infinity Fabric
  • Distributed inference/training or HPC experience
  • Semiconductor or hardwareadjacent experience

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