Senior SWQA Test Development Engineer - GPU Communications Libraries
NVIDIA
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
What you’ll be doing:
Be responsible for running test cases to validate NVIDIA GPU Communications Libraries (NCCL, NVSHMEM, UCX, GDRCopy, GPUDirect RDMA etc).
Be responsible to automate test cases and maintain the automation scripts.
Collaborate with Developer, PM, marketing, and engineering teams on crafting test plan and implementing validation.
You will assist in the architecture, crafting and implementing of SWQA test frameworks.
Be responsible for code coverage improvement and code complexity optimization.
What we need to see:
BS or higher degree in CS/EE/CE or equivalent experience
5+ years of relevant experience
Seasoned software QA or software testing background; test infrastructure and strong analysis skills
Be proficient in scripting language (Python, Perl, bash)
Solid experience with AI development tools for test development and automation
Knowledge of basic networking concepts
UNIX/Linux experience is required
Experiences in C/C++ is required
Ability to work independently and leadership skills as well as experience in using quality mindset to drive improvements
Proficient oral and written English
Ways to stand out from the crowd:
Experience with CUDA programming and NVIDIA GPUs
Knowledge of high-performance networks like InfiniBand, RoCE, etc
Experience with CSPs (AWS, Google Cloud, Oracle Cloud Infrastructure, Microsoft Azure), and HPC cluster, slurm, ansible, etc
Prior experience with virtualization technologies (KVM, HyperV, VMWARE, OpenStack, Docker, Kubernetes)
Experience with Deep Learning Frameworks such as PyTorch, TensorFlow, etc