🤖 15+ AI Agents working for you. Find jobs, score and update resumes, cover letter, interview questions, missing keywords, and lots more.
Cookies & analytics consent
We serve candidates globally, so we only activate Google Tag Manager and other analytics after you opt in. This keeps us aligned with GDPR/UK DPA, ePrivacy, LGPD, and similar rules. Essential features still run without analytics cookies.
Read how we use data in our Privacy Policy and Terms of Service.
🤖 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: Software Test Development Engineer, mid-to-senior level (5+ years QC/automation background)
Stack/tools: Python/Perl/Bash scripting; UNIX/Linux; C/C++; virtualization (VMs, Docker); test frameworks; AI tools; exposure to CUDA/NVIDIA GPU hardware; experience with tools like VectorCAST, Bullseye, Gcov, Coverity (nice-to-have)
Plan, design, execute, report, and automate test plans, cases, and reports; own product quality with global cross-functional teams
Manage bug lifecycle and drive cross-group solutions; assist architecture of test frameworks; automate test cases
Reproduce/verify customer issues; leverage AI-powered tools to boost efficiency and quality (test/script generation, defect detection, day-to-day support)
BS+ in CS/EE/CE or equivalent
5+ years in software quality assurance or test automation
Scripting (Python, Perl, Bash) and UNIX/Linux experience
C/C++ development, DevOps or test development experience
Experience with virtualization (VM/Docker); strong written/oral English
Ability to handle changing priorities in dynamic schedules
Experience with AI tools
Familiarity with NVIDIA GPUs (Tesla, Tegra, DGX)
CUDA/NV GPU computing experience
Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang) and AI workloads
Experience with VectorCAST, Bullseye, Gcov, Coverity; automation-driven workflow improvements
Location & w
We are looking for a Software Test development engineer in NVIDIA’s Deep Learning SWQA team. The position is in NVIDIA Deep Learning Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s Deep Learning 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. Utilize AI-powered tools to improve efficiency and quality, including test case/plan/script generation, defect detection, CBTP, bug fixing and day to day assistance.
What We Need To See
BS or higher degree in CS/EE/CE or equivalent experience. 5+ years of software quality assurance or test automation background with knowledge of test infrastructure and strong analysis skills. Scripting language (Python, Perl, Bash) knowledge and UNIX/Linux experience. Good C/C++ software development, DevOps or test development experience. Good user/development experiences of virtualization like VM & Docker container. Excellent English written and oral communication skills. Able to juggle conflicting/changing priorities and maintain a positive attitude while experiencing challenging and dynamic schedules. Experience with AI tools.
Ways To Stand Out From The Crowd
Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc). Working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning. Experience in VectorCAST, Bullseye, Gcov, or Coverity tools. Automation experience. Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang, etc.) and familiar with running various AI workloads, proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation.
#deeplearning, , JR2007432