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Nvidia NVIDIA Internships PhD Computer Architecture Systems Research 
United States, California 
704549967

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US, CA, Santa Clara
time type
Full time
posted on
Posted 6 Days Ago
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your resume,expressing interest in one of our 2026 Computer Architecture and/or Systems focused Research Internships.review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.

society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading Computer Architecture and Systems teams.

Learn more about

What you will be doing:

  • Design and implement algorithms, hardware, and software solutions that advance computing, graphics, media processing, and related technologies central to NVIDIA's innovation.

  • Collaborate with other team members, teams, and/or external researchers.

  • Transfer your research to product groups to enable new products or types of products. Deliverable results include prototypes, patents, products, and/or publishing original research.

What we need to see:

  • Must be actively enrolled in a university pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field, for the entire duration of the internship.

  • Depending on the internship, prior experience or knowledge requirements couldinclude the following programming skills and technologies:

  • C, C++, Perl, Python, CUDA.

  • Strong background in research with publications at top conferences.

  • Excellent communication and collaboration skills.

Potential internships require research experience in at least one of the following areas:

Hardware-software co-design

Computer Architecture

  • GPU and Multi-GPU Architecture

  • Scalable memory systems

  • System-level GPU scheduling

  • Power, performance, and energy-efficiency in large-scale systems

Programming Systems

  • Parallel Computing

  • GPU-accelerated workloads

  • Distributed Computing

High-Performance Networking and Interconnects

  • Large-scale GPU networking

  • Topologies, routing, and congestion control

  • Circuits and microarchitecture for network controllers and switches

VLSI and Electronic Design Automation (EDA)

  • GPU Accelerated EDA

Systems for AI/ML

  • Systems Infrastructure for large LLM training and inference

  • ML for hardware systems (ML for EDA)

and other helpful student resources related to our latest technologies and endeavors.

You will also be eligible for Intern

Applications are accepted on an ongoingbasis.NVIDIA

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