AI Developer Intern — Interconnect Hardware Frontend, 2027
At a glance
Junior AI Engineering role at NVIDIA. China, Shanghai · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build a prototype around an RTL, verification, debug, code-understanding, or design-review problem, then refine it with its users
- Test AI tools, models, and agents on engineering tasks and help decide which capabilities merit adoption
- Create reusable scripts or small automation that improves the speed or reliability of a hardware workflow
- Work with hardware engineers to turn a practical pain point into an AI use case, then adjust it using feedback
- Share results, including what failed, so the team can choose where to experiment next
What you bring
- You are studying electrical engineering, computer engineering, computer science, or a related subject at the bachelor’s, master’s, or doctoral level
- You have solid Python and software engineering skills, with experience creating scripts, tools, or automation
- You have encountered RTL, verification, debug, or design-methodology work through study, research, projects, or internships
- You understand hardware well enough to assess whether an AI-supported solution is technically sound and useful
Who this fits
This internship suits a student who can work independently, communicate clearly, and collaborate with hardware engineers. Experience with LLM tools or agents, frontend hardware projects, or interconnect and memory-related silicon work may help you stand out.
From the employer
NVIDIA's MSS-Interconnect frontend team is looking for an intern to help explore where modern AI can create real value for RTL, verification, debug, and design-review workflows. During your internship, you will take a focused AI-for-hardware use case from idea to working prototype, evaluate current AI tools and agents on real engineering problems, and help the team learn which capabilities are worth adopting. You will work alongside RTL, verification, CAD, and methodology engineers on a global, cross-site team.
What you'll be doing:
- Prototype AI-assisted workflows for a focused hardware frontend problem — RTL, verification, debug, code understanding, or design review — and iterate with the engineers who use it.
- Evaluate new AI tools, models, and agent capabilities on real engineering tasks, and help assess which are worth adopting.
- Build small, reusable tools or automation that make a concrete workflow faster or more reliable.
- Partner with hardware engineers to turn a real pain point into a practical AI use case, and refine it based on usage and feedback.
- Share what works and what doesn't so the team can decide where to experiment and invest further.
What we need to see:
- 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 some experience building automation, scripts, or tools.
- Exposure to hardware frontend workflows — RTL design, verification, debug, or design methodology — through coursework, projects, research, or prior internships.
- Enough hardware understanding to judge whether an AI-assisted solution is actually useful and technically sound.
- Strong problem-solving, communication, and a collaborative, self-driven attitude.
Ways to stand out from the crowd:
- Hands-on experience building or using LLM-based tools, agents, or AI-assisted workflows — in coursework, research, or personal projects.
- Strong familiarity with modern AI tooling and good judgment on which new tools are worth trying.
- Coursework, research, or projects in frontend hardware development or debug-intensive workflows.
- Familiarity with Interconnect, NoC, Memory System, bus-fabric, or related silicon domains.
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