We are looking fa 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 performs tests to validate robustness measure the performance of NVIDIA‘s Deep Learning software GPU Infrastructure fautonomous driving, healthcare, speech recognition, natural language processing, a wide variety of other AI scenarios. This team collaborates with multiple AI product teams to develop new products; derive improve complex test plans; improve our workflow processes fa diverse range of GPU computing platforms. You should grow with being in the critical path supporting developers working fbillion-dollar business lines as well as intimately understanding the values of responsiveness, thoroughness teamwork. You should constantly foster 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 understthe test requirements take ownership of product quality.
Plan/design/execute/report/automate test plan/test case/test reports.
Manage bug lifecycle co-work with inter-groups to drive fsolutions.
Automate test cases assist in the architecture, crafting implementing of test frameworks.
In-house repro verify customer issues/fixes.
Utilize AI-powered tools to improve efficiency quality, including test case/plan/script generation, defect detection, CBTP, bug fixing day to day assistance.
【What we need to see】
BS higher degree in CS/EE/CE equivalent experience.
5+ years of software quality assurance test automation background with knowledge of test infrastructure strong analysis skills.
Scripting language (Python, Perl, Bash) knowledge UNIX/Linux experience.
Good C/C++ software development, DevOps test development experience.
Good user/development experiences of virtualization like VM & Docker container.
Excellent English writtenal communication skills.
Able to juggle conflicting/changing priorities maintain a positive attitude while experiencing challenging dynamic schedules.
Experience with AI tools.
【Ways to stout from the crowd】
Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc).
Understanding working knowledge with any Deep Learning Framework especially in end to end customer scenarios.
Working knowledge of NVIDIA GPU Computing (CUDA) CUDA libraries fDeep Learning.
Experience in VectorCAST, Bullseye, Gcov, Coverity tools.
Automation experience.
Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows enhance process automation.
