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$144,375–$235,750.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
NVIDIA 2027 Summer Internships: Ph.D. Engineering interns join the Computer Architecture and Systems team to design and implement novel ideas in GPU and CPU architectures, operating systems, AI systems, and distributed systems. The role involves collaborating with internal teams and external researchers to advance computing, graphics, media processing, and related technologies. Deliverables include prototypes, patents, products, or original research publications. Candidates must be pursuing a Ph.D. in Computer Science, Electrical Engineering, or a related field. Required technical skills may include C, C++, Python, and CUDA. Research areas of interest include scalable memory systems, interconnects, power efficiency, hardware-software co-design, LLM training infrastructure, compiler optimization, high-performance networking, and VLSI. The work focuses on solving complex problems in accelerated computing to advance the core technologies central to NVIDIA's business operations and product development.
Design and implement novel ideas in GPU/CPU architectures, operating systems, and distributed systems.
Develop technologies for graphics, media processing, and other core business areas.
Translate research findings into practical products or new product types for production teams.
Produce tangible deliverables including prototypes, patents, and published original research.
Optimize performance, power, and energy efficiency in large-scale computing systems.
Develop hardware-software co-designs specifically for AI/ML workloads and infrastructure.
Create and optimize GPU-accelerated algorithms and parallel computing programming models.
Design high-performance networking solutions including topologies, routing, and congestion control.
What we're looking for
Must be actively enrolled in a university pursuing a Ph.D. degree in Computer Science, Electrical Engineering, or a related field.
Must provide an anticipated graduation date (month and year) on the resume or CV.
Proficiency in programming languages and technologies including C, C++, Python, and CUDA.
Strong background in research with publications at top conferences.
Excellent communication and collaboration skills.
Research experience in at least one area such as GPU/CPU architecture, AI systems, distributed systems, or high-performance networking (depending on the internship).
CUDA
C++
C
Python
GPU Architecture
CPU Architecture
Operating Systems
Distributed Systems
AI/ML Systems
LLM Training
VLSI
EDA
Parallel Computing
High-Performance Networking
Hardware-Software Co-design
Memory Systems
Multi-GPU Architecture
Python
C++
CUDA
PyTorch
TensorFlow
Deep Learning
Large-scale Model Training
LLMs
Multimodal Models
Sensor Fusion
Digital Twins
Simulation
Model Distillation
Inference Optimization
Control Barrier Functions
Computer Vision
Deep Learning
Python
C++
CUDA
PyTorch
TensorFlow
3D Vision
Scene Understanding
Neural Rendering
Diffusion Models
NeRFs
Vision Transformers
Neural Architecture Search
Synthetic Data Generation
Python
C++
CUDA
PyTorch
JAX
TensorFlow
Deep Learning
Generative AI
Computer Vision
Large Language Models
Diffusion Models
Multimodal Foundation Models
World Models
Action-Based Transformers
Physics-Based Simulation
Synthetic Data Generation
Partial Differential Equations