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# Senior Software Engineer, CUTLASS Performance

- **Company:** NVIDIA Corporation
- **Location:** Redmond, WA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Computer Programming, Computer Engineering, Python (Programming Language), Open Source Technology, Performance Tuning, Software Engineering, Scripting, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Parallel Computation, Information Technology, SGLang, VLLM, CUTLASS, Software Performance
- **Published:** October 20, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-cutlass-performance-redmond-wa--ed7004ae-db82-4a71-b56b-894b2ae5544e

## About the Role

Software Engineering, Linear Algebra, Open Source, Ecosystems, Deep Learning, Python Programming/Scripting Language, Mathematics, GPU (Graphics Processing Unit), C++ Programming Language, Benchmarking, * 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 in developing performance models and performance regression systems.

## Description

* 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.