16 hours ago
Shanghai, ChinaIntern
Responsibilities
- Prototype AI-assisted workflows for RTL, verification, debug, code understanding, or design review.
- Evaluate AI tools, models, and agent capabilities on real engineering tasks.
- Build small, reusable Python tools or automation to improve hardware frontend workflows.
- Partner with hardware engineers to turn pain points into practical AI use cases and refine them using feedback.
- Communicate findings about effective and ineffective approaches to guide future experimentation and investment.
Requirements
- Currently pursuing a BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
- Solid Python and software engineering fundamentals with experience building automation, scripts, or tools.
- Exposure to hardware frontend workflows such as RTL design, verification, debug, or design methodology.
- Sufficient hardware understanding to assess whether AI-assisted solutions are useful and technically sound.
- Strong problem-solving, communication, collaboration, and self-directed working skills.
- Hands-on experience with LLM-based tools, agents, or AI-assisted workflows is preferred.
- Familiarity with modern AI tooling, frontend hardware development, debug-intensive workflows, Interconnect, NoC, Memory System, bus-fabric, or related silicon domains is preferred.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.