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# Senior Software Engineer, CUTLASS Platform

- **Company:** NVIDIA Ltd.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Code Generation, Nvidia CUDA, Computer Programming, Computer Engineering, Software Debugging, Python (Programming Language), Software Engineering, Graphics Processing Unit (GPU), Deep Learning, Parallel Computation, Backend, Information Technology, Software Coding
- **Published:** June 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=69616e0da0cad1d3

## About the Role

Do you have experience in Software coding?, Do you have a Master's degree?, If you are passionate about designing abstractions for Tensor Core and related GPU hardware features in MLIR, Python, and C++ that enable writing high performance kernels, apply to join the CUTLASS team today!, * Masters or PhD degree in Computer Science, Computer Engineering, or related field (or equivalent experience).

 * 3+ years of relevant industry experience.
 * Strong proficiency in C++ programming and software design, including debugging, performance evaluation, and testing.
 * Experience working with high-performance code generation and knowledge of compiler transformations and optimizations.
 * A deep understanding of computer architecture and parallel computing programming models.

Ways to stand out from the crowd:

 * Experience writing high-performance kernels at low levels of abstractions like NVVM/ PTX for GPUs or other similar parallel processing architectures.
 * Hands-on compiler design experience, particularly in MLIR.
 * Understanding of deep learning models, algorithms, and frameworks.

## Description

* Develop core components of the CUTLASS platform including Tensor Core MMAs, copies, synchronization barriers, schedulers, and other GPU hardware features in CUDA C++ and CUTLASS Python DSL.
 * Contribute to the advancement of the MLIR-based backend compiler stack for the CUTLASS Python DSL by designing dialects and associated compiler passes.
 * Author example kernels utilizing CUTLASS abstractions to showcase the use of novel GPU hardware features that are crucial for achieving high performance.
 * Collaborate with GPU architecture, CUDA, and NVVM/PTX compiler teams to provide feedback on programming models and to assess the performance of future GPU hardware features.


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