PhD Research Intern, AI Accelerator Design and VLSI - 2027

NVIDIA is hiring a PhD Research Intern, AI Accelerator Design and VLSI - 2027 in Santa Clara, United States. It pays $38-$94/hr and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include PyTorch, Python and SystemVerilog.
Level classifies it as an internship.
Level lists 2,600+ other open roles at NVIDIA.

AI in this role

Conducts PhD-level research as an intern focusing on AI accelerator design, VLSI methodology, and HW/SW co-design.

PyTorchPythonSystemVerilogCHls

ai-accelerator-designvlsihw-sw-co-designmachine-learningasic-design

NVIDIA Research is seeking PhD-level students to work as research interns at the intersection of AI HW/SW Co-Design, Hardware Accelerator Architecture, IC Design Methodology, and VLSI Design. Ideal candidates will have a broad perspective across areas including machine learning fundamentals, quantization and numerical methods for machine learning model optimization, digital VLSI circuits for computer arithmetic, high-productivity VLSI design and verification methodologies including applications of agentic AI to hardware design, AI hardware micro-architecture, and VLSI methodology and implementation.

What You’ll Be Doing:

Accelerator Design: Contribute to novel research advancing the state-of-the-art in the design of accelerators for AI and other high-impact workloads.

VLSI: Research creative and innovative ASIC and VLSI design techniques and/or novel digital VLSI circuits. Apply machine learning, agentic AI, and state-of-the-art tools and methodologies to automated ASIC and VLSI design tool flows.

AI HW/SW Co-Design: Research and develop creative and innovative numerical methods for quantization, sparsity, or tensor decomposition grounded in computer arithmetic fundamentals and digital VLSI circuits.

Collaborate on the development of research prototype testchips.

Collaborate with AI researchers and hardware team members in research and product teams.

Plan to publish and present your original research.

What We Need To See:

Pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field.

Publication records in leading ML, architecture, VLSI, or circuits conferences.

Excellent programming skills in Python/PyTorch and hardware design languages such as SystemVerilog/C++; Experience with High-Level Synthesis (HLS) tools is a plus.

VLSI Implementation Skills: Experience in hardware design with proficiency with modern EDA tool flows; Tapeout experience is a plus.

Excellent self-motivation, a high degree of creativity, and a passion for research, collaboration skills, and the ability to work effectively within a research team.

Excellent written and verbal communication skills, with proven experience communicating technical work (e.g., academic presentations, poster sessions); ability to synthesize and explain complex technical concepts.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA has some of the most forward-thinking and hardworking people in the world inventing the future for us. Are you a creative and collaborative researcher interested in seeking new challenges? If so, we want to hear from you!

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 38 USD – 94 USD.

You will also be eligible for Intern benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

PhD Research Intern, AI Accelerator Design and VLSI - 2027 at NVIDIA rates 95 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.

Classification

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.

Key facts

PhD Research Intern, AI Accelerator Design and VLSI - 2027 is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed pay is $38-$94/hr.
The role is based in US, CA, Santa Clara.
The job was posted on September 29, 2026.
Seniority: intern.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI Accelerator DesignVlsiHw Sw Co DesignMachine learningAsic DesignPyTorchPythonSystemVerilog

Questions you could be asked

Tell me about a project where ai accelerator design was part of your work. What did you do?
Tell me about a project where vlsi was part of your work. What did you do?
Tell me about a project where hw sw co design was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where asic design was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Accelerator Design, Vlsi, Hw Sw Co Design, Machine learning, and Asic Design. 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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