英伟达Developer Technology Engineering Intern - 2026
任职要求
• Pursuing MS or PhD from a leading University in an engineering or Computer Science related discipline. • Strong knowledge of C/C++ and/or Fortran. • Knowledge of software design, programming techniques, and algorithms. • Knowledge of LLM training/inference optimization, including but not limited to development and optimization experience in distribu…
工作职责
• 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...
• You will work and develop state of the art techniques in deep learning, graphs, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures • You will provide the best AI solutions using GPUs working directly with key customers • Collaborate closely with the architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models
We’re working on the next generation of recommendation tools and pushing the boundaries of accelerating model training and inference on GPU. You’ll join a team of ML, HPC and Software Engineers and Applied Researcher developing a framework designed to make the productization of GPU-based recommender systems as simple and fast as possible. What you’ll be doing: In your role as CUDA Engineer Intern you will be profiling and investigating the performance of optimized code together within our HPC team. Part of this job will be to perform tests, unit tests and validate the numerical performance and correctness of the code. You will discuss your approach and results together with our CUDA engineers.
• You will work and develop state of the art techniques in deep learning, graphs, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures • You will provide the best AI solutions using GPUs working directly with key customers • Collaborate closely with the architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models
As a valued member of the team, you will be involved in the technical design and implementation of numerous features working in an agile environment. In this role you can expect to:• Create developer tools features for NVIDIA GPUs that enables developers to quickly iterate on optimizations to build fast graphics applications. • Write fast, effective, maintainable, reliable and well documented code. • Effectively estimate and prioritize tasks in order to build a realistic delivery schedule. • Provide peer reviews to other engineers including feedback on performance, scalability and correctness. • Drive technology discussions and provide valuable feedback about the architecture as a domain expert. • Document requirements and designs, and review documents with stakeholders. • Demonstrate growth in technical and non-technical abilities. • Meet with the QA Department to develop a test plan for new features.