Description
We are seeking a Performance Engineer Intern to support our growing investments in performance testing of various NVIDIA's 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 GPUs, CPUs, interconnects and software together act as the brains of the AI factories that power it.
You will be part of the global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art AI 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.
Responsibilities:
- Benchmark, profile, and analyze AI and HPC workloads on NVIDIA supercomputers and distributed systems - LLM and VLM training and inference, agentic workloads, world models, and Physical AI for robotics.
- Aggregate and produce written reports with the testing data for internal sales, marketing, SW, and HW teams.
- Build benchmark automation with AI agents and agentic coding tools. Ensure the whole process and results are reliable and reproducible.
- Collaborate with internal teams to debug and resolve performance issues.
- Set up and configure systems with appropriate hardware and software to run benchmarks.
Requirements:
- Currently pursuing a bachelor's degree (or higher) in Computer Science, Electrical Engineering, or a related field.
- Experienced in programming and debugging with scripting languages such as Python or Unix shell. Hands-on experience with Linux based systems. Familiarity using a container platform such as Docker.
- Fluent with AI coding assistants and agentic workflows, with the judgment to verify what they produce rather than defer to them.
- Hands-on experience with inference frameworks such as TensorRT-LLM, vLLM, or SGLang. Or experience in AI model inference deployment and training.
- Strong data analysis skills and the ability to summarize findings in a written report.
- Strong English verbal and written skills to improve collaboration with coworkers.
Nice to Have:
- Experience with CI/CD pipelines and modern DevOps practices. Familiar with cloud provisioning and scheduling tools (Kubernetes, Slurm).
- Experience building agent workflows, MCP tools, or reusable agent skills that other people use.
- Curiosity about GPUs, TPUs, cloud and performance benchmarking.
- Background in system-level problem solving.