NVIDIAPerformance Engineering Intern, Deep Learning and HPC - 2027
NVIDIA is hiring a Performance Engineering Intern, Deep Learning and HPC - 2027 in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include vLLM, Python and Docker.
- Level classifies it as an internship.
- Level lists 2,700+ other open roles at NVIDIA.
- The role was posted in the last few days.
AI in this role
Benchmark and profile state-of-the-art AI, LLM, and VLM workloads on NVIDIA supercomputers and distributed systems.
We are now looking for 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. 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 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.
What we need to see:
- 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.
Ways to stand out from the crowd:
- 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.
We have some of the most forward thinking and hardworking people in the world working for us and our best-in-class engineering teams are rapidly growing. We are building a team that will help shape the future of data center computing. If you are passionate about new technologies, care about improving efficiency and quality, and want to be at the forefront of AI & HPC & Gaming, we would love for you to join us
How we rate this
Performance Engineering Intern, Deep Learning and HPC - 2027 at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where benchmarking was part of your work. What did you do?
- Tell me about a project where profiling was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where performance engineering was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Benchmarking, Profiling, Deep learning, and Performance Engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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