Deep Learning Performance Architect
NVIDIA
Deep Learning Performance Architect
NVIDIA
Designs the chips and software that power AI
Are you excited about building world-class quality systems for advanced GPU software? Do you enjoy combining automation, product validation, andcode 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 everycodechange isvalidatedforcorrectness, and the product is ready for shipping at any time.
Whatyou'llbe doing:
Develop and evolve the NVIDIA CUTLASS DSL testing framework for next-generation GPU software
Define, refine, and execute robust product test strategiesfor 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 ofrelevant 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 communicationskills 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
NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.
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