英伟达Performance Engineering Intern - 2026

1.性能分析与评估：深入分析内存模组的工作原理和架构，结合实际测量数据，探究影响内存性能的各种因素，建立性能分析模型； 2.性能优化策略制定与实施: 根据性能分析结果，制定切实可行的内存模组性能优化策略，涉及硬件和软件多个层面； 3.跨部门协作与沟通：与内存模组设计，测试团队密切合作，将性能优化的需求和建议融入到产品设计的全过程中，确保设计方案在满足性能要求； 4.技术跟踪与创新：密切关注国内外内存技术和性能优化方法和相关行业标准的更新，及时掌握新技术、新方法和新趋势。

We are now looking for a Performance Engineer Intern to support our growing investments in perf testing of various company datacenter products and applications. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world, all while striving to deliver the highest possible performance of our products.You will be part of global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art datacenter applications and products. We also work to develop new scripts that enhance the team’s ability to gather data through automation and designing efficient processes for testing a wide variety of applications and hardware. The data that we collect drives marketing/sales collaterals as well as engineering studies for current and future products. You will have the opportunity to work with multi-functional teams and in a dynamic environment where multiple projects will be active at once and priorities may shift frequently. What you’ll be doing: • Benchmark, profile, and analyze the performance of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC) on NVIDIA supercomputers and distributed systems. • Aggregate and produce written and visual reports with the testing data for internal sales, marketing, SW, and HW teams • Setup and configure systems with appropriate hardware and software to run benchmarks • Collaborate with internal teams to debug and improve performance issues • Develop Python scripts to automate the testing of various applications • Assist with the development of tools and processes that improve our ability to perform automated testing

We are now looking for a Performance Engineer Intern to support our growing investments in perf testing of various company datacenter products and applications. Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world, all while striving to deliver the highest possible performance of our products.You will be part of global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art datacenter applications and products. We also work to develop infrastructures and solutions that enhance the team’s ability to gather data through automation and designing efficient processes for testing a wide variety of applications and hardware. The data that we collect drives marketing/sales collaterals as well as engineering studies for future products. You will have the opportunity to work with multi-functional teams and in a dynamic environment where multiple projects will be active at once and priorities may shift frequently. What you’ll be doing: • Benchmark, profile, and analyze the performance of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC) on NVIDIA supercomputers and distributed systems. • Aggregate and produce written reports with the testing data for internal sales, marketing, SW, and HW teams. • Develop Python scripts to automate the testing of various applications. • Collaborate with internal teams to debug and improve performance issues. • Assist with the development of tools and processes that improve our ability to perform automated testing. • Setup and configure systems with appropriate hardware and software to run benchmarks.

• Writing highly tuned compute kernels to perform core deep learning operations (e.g. matrix multiplies, convolutions, normalizations) • Following general software engineering best practices including support for regression testing and CI/CD flows • Collaborating with teams across NVIDIA:• CUDA compiler team on generating optimal assembly code • Deep learning training and inference performance teams on which layers require optimization • Hardware and architecture teams on the programming model for new deep learning hardware features