Deep Learning Test Development Engineer Architect
We are looking for a Software Test Development Architect in NVIDIA’s Deep Learning SWQA team.
The position is in NVIDIA Deep Learning Software Quality Assurance team that defines, develops test strategies to improve quality 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. This position collaborates with multiple AI product teams to develop new products; identify test gaps in test plans, improve test coverage, 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 develop 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 DL engineering teams to develop a keen understanding of DL QA goals, test strategies, and technical needs.
Collaborate with diverse inter-groups, including DL Researchers, Product, and engineering teams to identify gaps, and improve processing.
Lead bug lifecycle and co-work with QA test developers to analyze customer bugs and user scenarios to improve test coverages.
What we need to see:
MS/PhD (PhD preferred) in CS, EE, Math or closely related fields or equivalent experience.
10+ years of software development experience in DL/ML, DL Framework (Especially JAX and PyTorch), Neural Networks, DL Service deployment, user scenario analysis and SDKs.
Able to design test strategies for diverse DL products to optimize test plans and identify the most essential and risky use cases.
Excellent C/C++, Python programming skills; Strong written and oral communications skills in English.
Ways to stand out from the crowd:
Be familiar with deep neural network training, inference, optimization in typical Frameworks.
Experience software development with popular AI models (e.g., LLM models)
Background with GPU computing and parallel programming such as CUDA/OpenCL.