NVIDIA 2027 Summer Internships: Ph.D. Engineering

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NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting 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. We’re seeking strategic, ambitious, hard-working, collaborative, and creative individuals who are passionate about helping us tackle challenges no one else can solve. 

Learn more about Research at NVIDIA. 

What you will be doing: 

• Design and implement novel ideas in GPU and CPU architectures, systems architectures, operating systems, AI systems, and distributed systems that advance computing, graphics, media processing, and related technologies central to NVIDIA's business. 

• 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 Ph.D. degree in Computer Science, Electrical Engineering, or a related field, for the full duration of the internship; anticipated graduation date (month and year) must be clearly indicated on a resume or CV to be considered.     

• Depending on the internship, prior experience or knowledge requirements could include the following programming skills and technologies: C, C++, 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: 

• Chip-level and System-level Architecture 

GPU and Multi-GPU Architecture Scalable memory systems and new memory technologies 

Scalable on-chip and off-chip interconnects 

Chip-level and system-level scheduling 

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

Specialized accelerators for workloads like AI algorithms, crypto algorithms, databases, etc. 

Hardware-software co-design 

• Systems for AI/ML 

Systems Infrastructure for large LLM training and inference 

Systems/AI algorithms codesign (e.g., for sparsity) \

AI/ML for systems (hardware design, code optimization, etc.) 

ML for EDA 

• Programming Systems 

GPU-accelerated algorithms 

Languages and programming models for parallel computing 

Optimizing compilers and AI-based performance assistants 

Distributed runtime systems 

Systems software and operating system interfaces 

Optimizing GPU-accelerated workloads 

Compilers and code verification 

• High-Performance Networking and Interconnects 

Large-scale GPU networking 

Topologies, routing, and congestion control 

Networking techniques at the intersection of scale-out and scale-up 

• VLSI and Electronic Design Automation (EDA) 

GPU Accelerated EDA 

Click here to learn more about NVIDIA, our early talent programs, benefits offered to students and other helpful student resources related to our latest technologies and endeavors. 

You will also be eligible for Intern benefits. ​

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.