The ideal candidate for the Research Intern position at NVIDIA must hold a PhD in Electrical Engineering, Computer Engineering, or a related field, have a strong publication record in leading conferences on DL for EDA or GPU accelerated EDA, possess deep knowledge in EDA/VLSI and deep learning, and exhibit excellent programming skills in Python, PyTorch, C++, or CUDA. They should also be able to work independently and have a strong publication record to distinguish themselves.

As a Research Intern at NVIDIA, you will utilize deep learning and GPU acceleration techniques to enhance EDA software and ASIC and VLSI design processes. You will collaborate with various teams, research and develop innovative algorithms, publish and present findings, and collaborate with external researchers and product teams.

Research & Development

Software Engineering

United States

San Francisco

Austin

Large

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.

Manufacturing & Electronics

NVIDIA

<ul><li>NVIDIA Design Automation research group is seeking research interns in the areas of AI for chip design and GPU accelerated EDA. Deep learning and GPU advancement are changing the landscape of chip design.&nbsp; In this internship, you will gain a broad perspective across areas including EDA algorithms and software, machine learning, and VLSI chip design methodology. </li><li>Specific areas of research interest include but are not limited to applications of supervised learning, unsupervised learning, reinforcement learning and GPU acceleration to EDA algorithms.</li></ul>

Currently Studying

NVIDIA Design Automation research group is seeking research interns in the areas of AI for chip design and GPU accelerated EDA. Deep learning and GPU advancement are changing the landscape of chip design.  In this internship, you will gain a broad perspective across areas including EDA algorithms and software, machine learning, and VLSI chip design methodology. Specific areas of research interest include but are not limited to applications of supervised learning, unsupervised learning, reinforcement learning and GPU acceleration to EDA algorithms.

Research Intern, Design Automation - Summer 2024

Requirements

Responsibilities

NVIDIA

Mission & Purpose