Cuda Software Engineer information
See salary details
$63.5K - $76.4K
2% of jobs
$76.4K - $89.3K
4% of jobs
$89.3K - $102.2K
6% of jobs
$102.2K - $115.1K
8% of jobs
$122.1K is the 25th percentile. Wages below this are outliers.
$115.1K - $128K
7% of jobs
The median wage is $144K / yr.
$141K - $153.9K
16% of jobs
$153.9K - $166.8K
2% of jobs
$172.1K is the 75th percentile. Wages above this are outliers.
$166.8K - $179.7K
26% of jobs
$179.7K - $192.6K
1% of jobs
$192.6K - $205.5K
8% of jobs
How much do cuda software engineer jobs pay per year?
As of Sep 9, 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.
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.
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.
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 popular job titles related to Cuda Software Engineer jobs?
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