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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.