Senior Software Test Development Engineer - Deep Learning

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Senior Software Test Development Engineer for their Deep Learning SWQA team. This role involves defining, developing, and executing tests to validate the robustness and performance of NVIDIA's Deep Learning software and GPU infrastructure across various AI applications. Responsibilities include collaborating with AI product teams, developing complex test plans, automating test cases, and managing the bug lifecycle. The ideal candidate has 6+ years of QA/test automation experience, scripting skills, C/C++ development, and understanding of Deep Learning frameworks and models, particularly in end-to-end customer scenarios.

What you'd actually do

  1. Work closely with global multi-functional teams to understand the test requirements and take ownership of product quality.
  2. Plan/design/implement/report/automate test plan/test case/test reports.
  3. Run bug lifecycle and co-work with inter-groups to work towards solutions.
  4. Automate test cases and assist in the architecture, crafting and implementing of test frameworks.
  5. In-house repro and verify customer issues/fixes.

Skills

Required

  • BS or higher in CS/EE/CE or equivalent experience
  • 6+ 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
  • Excellent English written and oral communication skills

Nice to have

  • Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.)
  • working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning
  • Background in building models and AI-based infrastructure to improve test automation
  • Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang, etc.) and familiar with running various AI workloads
  • Background in validating Data Center GPU based infrastructure (multi-GPUS, multi-nodes, cluster)
  • Experience in VectorCAST, Bullseye, Gcov, or Coverity tools

What the JD emphasized

  • 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.
  • Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang, etc.) and familiar with running various AI workloads

Other signals

  • 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
  • Experience with LLM inference frameworks