Senior Software Engineer, CUTLASS Performance

Nvidia

Actively hiring Posted this week Verified listing

Santa Clara, CA · Austin, TX · Hillsboro, OR · Durham, NC · Redmond, WA Posted 3 days ago $152,000–$241,500 / year

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $181k

This role $197k

$111k most similar roles pay here $256k

This role pays more than 65% of similar roles. Most pay $143,572–$217,725 — the shaded band above. At the midpoint, this role pays about $197k versus about $181k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 855 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 843 roles with salary data.

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At a glance

TL;DR

Join NVIDIA’s CUTLASS team as a senior software engineer to drive high-performance linear algebra and Tensor Core primitives for AI applications. You will benchmark state-of-the-art deep learning models’ inference and training passes, identify performance gaps, and suggest improvements or model adjustments. Develop tooling to automate the performance optimization loop and serve as the authoritative resource on kernel performance within the team, collaborating with GPU architecture, DL frameworks, and QA teams. Ideal candidates have a Masters or PhD in Computer Science or related field, 3+ years of industry experience, strong Python and C++ skills, expertise in software performance analysis, and hands-on experience with DL framework benchmarking and regression systems. This role involves deep technical work on cutting-edge AI technologies at scale.

Skills

Python C++ PyTorch JAX SGLang vLLM TRT-LLM GPU Deep Learning Performance Analysis Performance Optimization Computer Architecture CI/CD

What you'll do

Benchmark performance of deep learning models’ inference and training passes to identify GPU kernel opportunities.
Analyze gaps between theoretical and actual performance to suggest software or model improvements.
Develop tooling for automated benchmarking, analysis, and optimization of CUTLASS kernels in DL networks.
Serve as the team’s expert on kernel performance, engaging with cross-NVIDIA teams like GPU architecture and QA.
Create and maintain performance models and regression systems for continuous improvement.

What we're looking for

Masters or PhD in Computer Science/Engineering or equivalent experience.
3+ years of industry experience in software performance analysis.
Strong Python and C++ programming skills.
Deep understanding of computer architecture and GPUs.
Experience with benchmarking deep learning frameworks like PyTorch, JAX.
Development of performance models and regression systems for DL.
Engagement with cross-functional teams on GPU kernel optimization.

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