
Software Engineering Intern, CUDA Test Development - 2027
Shanghai, Shanghai, ChinaFull-timePosted 2w agoStill listed today
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Job overview
NVIDIA seeks a CUDA Test Development Software Intern to design and implement tests for CUDA libraries and drivers, automate testing, and analyze failures, working five days a week for at least one year while pursuing an advanced degree in computer science.
Skills & qualifications
Skills
Qualifications
Full job description
NVIDIA is the world leader in GPU Computing. We are passionate about four markets: Gaming, Automotive, Enterprise Graphics and HPC/Cloud Datacenters; in addition to our traditional OEM business. We are well positioned as the ‘AI Computing Company’, and our GPUs are the brains powering modern Deep Learning software frameworks, accelerated analytics, big data, modern data centers, smart cities, and driving autonomous vehicles. We have the most brilliant and talented people in the world working for us. If you are talented, bright, driven and if working with smart technical people across countries sounds interesting, this job is for you. Our team is mainly focusing on CUDA Automation testing, CUDA Safety and CUDA test development area and we are now looking for a CUDA Test Development Software Intern.
What you’ll be doing:
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Design and Implement tests for CUDA library and driver.
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Automate CUDA tests, design test plan and enable them in automation testing infrastructure.
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Triage test results, isolate test failures and improve test coverage.
What we need to see:
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Work 5 days a week for at least 1 year
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Pursuing MS or PhD degree from a leading university in computer science.
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Familiar with programming and debugging skills with C/C++ and Python.
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Interested in test cases development, tests automation and failure analysis.
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Experience in using AI to improve quality and productivity across the end-to-end QA workflow.
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Good QA sense, knowledge and experience in software testing.
Ways to stand out from the crowd:
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Hands-on experience developing AI skills and AI agents to solve real-world problems.
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Strong English communication and collaboration skills.
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Familiar with parallel programming, ideally CUDA C/C++, is a plus.
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Background with VectorCAST, Gcov or other dev tool is a plus.
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