Senior Software Test Development Engineer - Deep Learning

We are looking for a Software Test development engineer in NVIDIA’s Deep Learning SWQA team. The position is in NVIDIA Deep Learning and AI 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. We collaborate 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 collaboration. You should constantly champion 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 multi-functional teams to understand the test requirements and take ownership of product quality.

• Plan/design/implement/report/automate test plan/test case/test reports.

• Run bug lifecycle and co-work with inter-groups to work towards 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 experience.

• 8+ 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 or test development experience.

• Good user/development experiences of virtualization like VM & Docker container.

• Understanding and working knowledge with any Deep Learning Framework and models especially in end-to-end customer scenarios.

• Experience in validating Deep Learning software and Deep Learning models.

• Experience in using AI development tools for test plans creation, test cases development and test cases automation.

• Able to balance conflicting/changing priorities and maintain a positive attitude while experiencing ambitious and dynamic schedules.

• Excellent English written and oral communication skills.

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 building models and AI-based infrastructure to improve test automation.

• Background in validating Data Center GPU based infrastructure (multi-GPUS, multi-nodes, cluster).

• Experience in VectorCAST, Bullseye, Gcov, or Coverity tools.