英伟达Software Engineer, cuDNN - Deep Learning

• Develop production-quality software that ships as part of NVIDIA's AI software stack, including optimized large language model (LLM) support. • Analyze the performance of important workloads, tuning our current software, and proposing improvements for future software. • Work with cross-collaborative teams of deep learning software engineers and GPU architects to innovate across applications like generative AI, autonomous driving, computer vision, and recommender systems. • Adapt to the constantly evolving AI industry by being agile and excited to contribute across the codebase, including API design, software architecture, performance modeling, testing, and GPU kernel development.

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, crafting and implementing of test frameworks. • In-house repro and verify customer issues/fixes.

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, crafting and implementing of test frameworks. • In-house repro and verify customer issues/fixes.

• Working directly with key application developers (especially LLM) to understand the current and future problems they are solving, creating and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications. This includes training and inference optimization for large language models, directly contributing to frameworks such as Megatron and TRTLLM, SGLang, vLLM... • Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models, including by investigating impact on application performance and developer productivity. • Engaging in deep optimization of high-performance operators, involving but not limited to CUDA deep optimization, instruction and compiler optimization. These optimizations will directly support customers or be integrated into products like cuDNN, cuBLAS, and CUTLASS... • Some travel is required for conferences and for on-site visits with developers.