NVIDIA 2027 Summer Internships: Ph.D. Engineering

at Nvidia

USD 38-94 per hour

INTERN

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 3 Algorithms @ 3 CUDA @ 3 Communication @ 3 Distributed Systems @ 3 GPU @ 3 LLM @ 3 Machine Learning @ 3 Networking @ 3 Python @ 3 Robotics

Details

NVIDIA is seeking Ph.D. engineering interns to gain hands-on experience with its Computer Architecture and Systems teams. Interns will work on challenges in accelerated computing, AI, digital twins, robotics, self-driving cars, healthcare, gaming, and related technologies.

Responsibilities

Design and implement novel ideas in GPU and CPU architectures, systems architectures, operating systems, AI systems, and distributed systems.
Advance computing, graphics, media processing, and related technologies central to NVIDIA's business.
Collaborate with team members, other teams, and external researchers.
Transfer research to product groups to enable new products or product types.
Deliver prototypes, patents, products, and/or original research publications.

Requirements

Actively enrolled in a university Ph.D. program in Computer Science, Electrical Engineering, or a related field for the full duration of the internship.
Clearly indicate the anticipated graduation month and year on the resume or CV.
Depending on the internship, prior experience or knowledge may include C, C++, Python, and CUDA.
Strong research background with publications at top conferences.
Excellent communication and collaboration skills.
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 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/AI algorithm co-design, including sparsity
- AI/ML for systems, including hardware design and code optimization
- Machine learning for electronic design automation
- 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
- Topologies, routing, and congestion control
- Networking techniques at the intersection of scale-out and scale-up
- VLSI and electronic design automation
- GPU-accelerated EDA