NVIDIA
Technology
SeniorSoftwareEngineer,CUTLASSPerformance
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optimal for Senior candidates.
“Senior Software Engineer, CUTLASS Performance at NVIDIA. Skills: High-performance computing, Performance optimization, GPU kernel performance. Benchmark model performance. Identify GPU kernel opportunities”
Industry & Context.
Performance optimization
What They're Looking For.
Must Have
Masters or PhD degree, 3+ years industry experience, Python programming skills, C++ programming skills, Software performance analysis, Software performance optimization, Computer architecture understanding, GPU familiarity, Parallel processing familiarity
Nice to Have
Deep understanding of DL model architectures, Hands-on performance benchmarking, Experience developing performance models, Experience developing performance regression systems
What You'll Do.
Benchmark model performance
Identify GPU kernel opportunities
Identify fusion opportunities
Identify performance gaps
Suggest software improvements
Suggest model adjustments
Automate benchmarking
Automate optimization
Push CUTLASS performance limit
Act as kernel performance resource
Engage with GPU architecture teams
Engage with DL frameworks teams
How You'll Work.
Team & Collaboration
GPU architecture teams; DL frameworks teams; QA teams
Full Job Description
NVIDIA's high-performance computing platforms are powering the AI revolution across many applications and industries. Within our software stack, [_CUTLASS_](https://github.com/NVIDIA/cutlass) stands out as a popular open-source ecosystem dedicated to high-performance linear algebra and Tensor Core primitives. Since 2017, it has provided the community with C++ and Python abstractions to implement custom matrix multiply (GEMM) and related math and deep learning computations on NVIDIA GPUs. If you are enthusiastic about performance and eager to help bridge the gap between current performance and what’s theoretically possible, apply to join the CUTLASS team today! **What you 'll be doing:** * Benchmark the performance of state-of-the-art deep learning models’ inference and training passes to identify key GPU kernel and fusion opportunities. * Identify gaps between theoretical and realized performance, and suggest software improvements or model adjustments to resolve them. * Develop tooling to automate the benchmarking, analysis, and performance optimization loop to push the limit of CUTLASS kernel performance within DL networks. * Be the authoritative resource on kernel performance in the team and engage with teams across NVIDIA including GPU architecture, DL frameworks, and QA as the performance representative for the CUTLASS team. **What we need to see:** * Masters or PhD degree in Computer Science, Computer Engineering, or related field (or equivalent experience). * 3+ years of relevant industry experience. * Strong programming skills in Python and C++. * Experience in software performance analysis and optimization. * Deep understanding of computer architecture and familiarity with GPUs or similar parallel processing architectures. **Ways to stand out from the crowd:** * Deep understanding of state-of-the art DL model architectures. * Hands-on experience with performance benchmarking of DL frameworks like PyTorch, JAX, SGLang, vLLM, TRT-LLM, or others. * Experience i
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