NVIDIA 2027 Internships: Ph.D. Research Computer Architecture and Systems

Details

NVIDIA's Ph.D. Research Computer Architecture and Systems internships offer hands-on experience with industry-leading computer architecture and systems teams. Interns will work on research addressing challenges in accelerated computing, AI, digital twins, graphics, media processing, robotics, self-driving cars, healthcare, climate change, and virtual worlds.

Responsibilities

• Design and implement novel ideas in GPU and CPU architectures, systems architectures, operating systems, AI systems, and distributed systems.
• Collaborate with team members, other teams, and external researchers.
• Transfer research to product groups to enable new products or product categories.
• Deliver prototypes, patents, products, and/or original research publications.

Requirements

• Must be actively enrolled in a university Ph.D. program in Computer Science, Electrical Engineering, or a related field for the full duration of the internship.
• The anticipated graduation month and year must be clearly indicated on the resume or CV.
• Depending on the internship, prior experience or knowledge may be required in C, C++, Python, and CUDA.
• Strong research background, with publications at top conferences.
• Excellent communication and collaboration skills.

Potential research areas include:

• 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 AI, cryptography, databases, and other workloads
• Hardware-software co-design
• Systems for AI and machine learning
• Systems infrastructure for large LLM training and inference
• Systems and AI algorithm co-design, including sparsity
• AI/ML for systems, including hardware design and code optimization
• Machine learning for electronic design automation (EDA)
• 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
• Large-scale GPU networking
• Network topologies, routing, and congestion control
• Networking techniques at the intersection of scale-out and scale-up
• GPU-accelerated EDA

Applications are reviewed on an ongoing basis. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.