NVIDIADeep Learning Performance Architect, CUTLASS DSL Testing
NVIDIA is hiring a Deep Learning Performance Architect, CUTLASS DSL Testing in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions Python, Cutlass and Mlir among its tools and skills.
- Level classes it as a senior role.
- NVIDIA has 2,600+ other open roles on Level.
AI in this role
Develop advanced GPU software testing frameworks and compilation pipelines for deep learning performance.
Are you excited about building world-class quality systems for advanced GPU software? Do you enjoy combining automation, product validation, and code analysis to support fast-moving compiler and kernel innovation? We are seeking a strong test engineer to develop the NVIDIA CUTLASS DSL testing framework, shape product test strategy, and ensure end-to-end code quality across the MLIR-based compilation pipeline. In this role, you will drive automated testing, and regression detection to make sure every code change is validated for correctness, and the product is ready for shipping at any time.
What you'll be doing:
Develop and evolve the NVIDIA CUTLASS DSL testing framework for next-generation GPU software
Define, refine, and execute robust product test strategies for shipping to the open-source community
Ensure end-to-end code quality across the MLIR-based compilation pipeline and related functional coverage infrastructure
Build automated testing, code coverage measurement, and regression detection workflows at scale
Partner with multiple teams to make sure every operator change meets a high bar for correctness, quality, and performance
What we need to see:
MS, PhD, or equivalent experience in Computer Science, Software Engineering, or a related field
3+ years of relevant work experience
Excellent Python and scripting skills
Strong experience developing and using test tools, with a solid understanding of software testing best practices
Hands-on experience with automated testing in GPU environments, including correctness testing, code coverage improvements, and regression detection
Strong communication skills and proven ability to collaborate effectively across teams
Ways to stand out from the crowd:
Familiarity with common AI agent technologies and applications
Experience in quality assurance of open-source products
How we rate this
Deep Learning Performance Architect, CUTLASS DSL Testing at NVIDIA rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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Skills and AI tools this role asks for
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- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where software testing was part of your work. What did you do?
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- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where automation was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Software Testing, GPU, Deep learning, and Automation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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