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🤖 15+ AI Agents working for you. Find jobs, score and update resumes, cover letter, interview questions, missing keywords, and lots more.
NVIDIA • Shanghai, Shanghai, China
Role & seniority
Stack/tools
Programming/scripting: Python, Bash; UNIX/Linux
Virtualization/containers: VM, Docker, Kubernetes
GPU/ML: NVIDIA GPU hardware, CUDA/cuDNN, NCCL (preferred), multi-GPU tests
AI frameworks/tools: TRT-LLM, vLLM, SGLang, LLM inference frameworks; AI coding tools (Cursor, Gemini, NotebookLM)
Test infra & capabilities: test plan/test case design, automation frameworks, bug lifecycle, test reports
QA tools: VectorCAST, Bullseye, Gcov, Coverity (nice-to-have)
Top 3 responsibilities
Plan, design, execute, report, and automate test plans, cases, and reports; own product quality
Manage bug lifecycle and coordinate with cross-functional groups to drive solutions
Automate test cases and contribute to test architecture/frameworks; reproduce/verify customer issues in-house
Must-have skills
BS or higher in CS/EE/CE or equivalent
5+ years in software quality assurance or test automation; strong test infrastructure knowledge
Proficiency in Python; UNIX/Linux experience
Good development/test development experience; strong analysis skills
Experience with virtualization/Docker/Kubernetes; ability to handle GPU P2P workloads
Excellent English communication (written and spoken)
Ability to adapt to changing priorities and dynamic schedules
Experience leveraging AI tools to improve efficiency and coverage
We are looking for a Software Test development engineer in NVIDIA’s AI SWQA team.
The position is in NVIDIA AI Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s AI software and GPU Infrastructure for autonomous driving, healthcare, speech recognition, natural language processing, and a wide variety of other AI scenarios. This team collaborates with multiple AI product teams to develop new products; derive and improve complex test plans; and improve our workflow processes for a diverse range of GPU computing platforms. You should grow with being in the critical path supporting developers working for billion-dollar business lines as well as intimately understanding the values of responsiveness, thoroughness and teamwork. You should constantly foster and implement efficiency improvements across your domain. Join the team which is building software which will be used by the entire world!
What You’ll Be Doing
Work closely with global cross-functional teams to understand the test requirements and take ownership of product quality. Plan/design/execute/report/automate test plan/test case/test reports. Manage bug lifecycle and co-work with inter-groups to drive for solutions. Automate test cases and assist in the architecture, crafting and implementing of test frameworks. In-house repro and verify customer issues/fixes.
What We Need To See
BS or higher degree in CS/EE/CE or equivalent. 5+ years of software quality assurance or test automation background with knowledge of test infrastructure and strong analysis skills. Scripting language (Python, Bash) knowledge and UNIX/Linux experience. Good Python software development or test development experience. Good user/development experience of virtualization like VM & Docker container & k8s Excellent English written and oral communication skills. Multiple GPUs P2P workload developing/testing Able to juggle conflicting/changing priorities and maintain a positive attitude while experiencing challenging and dynamic schedules. Proven success in leveraging AI (development) tools to significantly improve efficiency, streamline workflows, enhance process automation, create test cases and increase code coverage. Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang, etc.) and familiar with running various AI workloads Experience with AI tools for coding (like Cursor, Gemini, NotebookLM)
Ways To Stand Out From The Crowd
Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.) Familiarity with multiple GPUs tools usage (NCCL / NIXL) Understanding and working knowledge with any Deep Learning Framework especially in end-to-end customer scenarios. Working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning like cuDNN Experience in VectorCAST, Bullseye, Gcov, or Coverity tools.
JR2007444
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