Performance Engineer
NVIDIA is hiring a Performance Engineer in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Works on AI ●●●○ is how Level rates this role: the daily work is on AI products, without building the model.
- The posting mentions Python, AI agents and fine tuning among its tools and skills.
- Level classes it as a mid-level role.
- NVIDIA has 2,700+ other open roles on Level.
AI in this role
Design benchmark methodologies and automation frameworks for AI, LLM, and graphics workloads to optimize system performance.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re 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. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
Are you ambitious and ready to make a significant impact in a dynamic, technology-focused company? At NVIDIA, we're looking for a Performance Engnieer to join our outstanding Perflab in Shanghai. This role offers an outstanding opportunity to work with innovative GPUs and optimize performance from the design stage through the entire product lifecycle. Join us and be part of a team that extends the state-of-the-art in gaming, professional visualization, Omniverse, efficiency!
What you will be doing:
Design, config and execute representative benchmark methodologies and real-world use cases that showcase product capabilities across AI/LLM, gaming, workstation, content-creation, and system workloads across servers, PCs, workstations, SoC and laptops.
Analyze performance metrics and system telemetry to identify bottlenecks, regressions, and optimization opportunities.
Produce clear, data-driven competitive analyses and technical reports that help internal and external stakeholders position NVIDIA products effectively.
Develop and debug automation scripts for various benchmark performance and system monitoring data collection on Windows, Linux and MacOS.
Build and maintain scalable automation frameworks, AI agents, dashboards, and data pipelines that improve testing efficiency, coverage, and analytical robustness.
What we need to see:
BS or MS degree
2+ years equivalent practical experience, in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field.
Strong Python skills or proficiency in another scripting or programming language.
Hands-on interest in AI/LLM workloads, PC gaming, workstation applications, and content-creation workflows.
Solid understanding of PC and server architecture.
Experience working with large datasets, and strong analytical and troubleshooting skills.
Excellent communication, organizational, time management, and task prioritization skills.
Ways to stand out from the crowd:
Familiarity with NVIDIA GPUs and NVIDIA Technologies.
Working knowledge of AI inference or training, finetuning concepts.
Experience building, benchmarking, or operating AI platforms and LLM inference stacks.
Experience developing AI agents or agentic workflows.
Knowledge of containerized and virtualized workflows, including Docker, Kubernetes, and virtual machines.
How we rate this
Performance Engineer at NVIDIA rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● 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?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a project where performance engineering was part of your work. What did you do?
- Tell me about a project where benchmarking was part of your work. What did you do?
- Tell me about a project where automation was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Fine-tuning, Performance Engineering, Benchmarking, and Automation. 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.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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