We are looking for a Software Test development engineer in NVIDIA’s AI SWQA team. The position is in NVIDIA AI Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s AI 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, implementing/enabling test for CI/CD.
In-house repro and verify customer issues/fixes.
What we need to see:
Master or higher degree in computer science or similar.
5+ years of software quality assurance or test automation background with knowledge of test infrastructure and strong analysis skills.
UNIX/Linux administrator and troubleshooting experience
Good Python software development or test development skillset. Be familiar with python CI/CD pipeline development
Direct development experience in AI tools/products or using AI for major features
Good user/development experience of virtualization like VM & Docker container & k8s & Slurm
Excellent English written and oral communication skills.
Microcontroller programming / developing backgroud
Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation.
Ways to stand out from the crowd:
Experience in using AI to automate/implement QA end-to-end workflow
Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.).
Understanding and working knowledge with Deep Learning large scale training backend like Pytorch, Nemotron and Inference backend like vLLM, SGLang.
Has working knowledge of RL Post Training 3P-OSS like VeRL, MILES
Working knowledge of CUDA libraries for Deep Learning like cuDNN and TRT-LLM
Skills Required
- Master or higher degree in computer science or similar
- 5+ years software quality assurance or test automation with knowledge of test infrastructure and strong analysis skills
- UNIX/Linux administration and troubleshooting experience
- Python software development or test development skillset and familiarity with Python CI/CD pipeline development
- Direct development experience in AI tools/products or using AI for major features
- Experience with virtualization and containerization: VM, Docker, Kubernetes (k8s), and Slurm
- Microcontroller programming / development background
- Proven success leveraging AI tools to improve efficiency, streamline workflows, or enhance process automation
- Excellent English written and oral communication skills
- Experience using AI to automate/implement QA end-to-end workflow
- Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX)
- Working knowledge of deep learning training and inference backends (PyTorch, Nemotron, vLLM, SGLang)
- Working knowledge of RL post-training OSS like VeRL and MILES
- Working knowledge of CUDA libraries for deep learning such as cuDNN and TensorRT-LLM
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
