NVIDIA 2026 Internships: PhD Autonomous Vehicles Research - US

NVIDIA•Us, CA

10d•$30 - $94

About The Position

By submitting your resume, you’re expressing interest in one of our 2026 Autonomous Vehicles focused Research Internships. We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities. 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 Autonomous Vehicles teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve. Learn more about Research at NVIDIA .

Requirements

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 could include the following programming skills and technologies: Python, C++, CUDA, Deep Learning Frameworks (PyTorch , TensorFlow , etc.)
Strong background in research with publications at top conferences.
Excellent communication and collaboration skills.
Potential i nternships require research experience in at least one of the following areas: Next-Generation AV Architectures Chain-of-Thought Reasoning Mixture-of-Experts Diffusion-LLMs Diffusion-based Trajectory Decoding Novel Policy Training Strategies Closed-loop Training Off-policy RL Online RL Enforcing Consistency Foundation and Multimodal Models Vision-language models Multimodal reasoning Spatial Multimodal Models Modality Alignment Model Scaling Synthetic data Inference Efficiency Inference Optimizations (e.g., parallel decoding, speculative decoding) Token Representations Model Distillation Simulation and Behavior Modeling Digital Twins Scenario Generation World Models Behavior/Traffic Modeling End-to-End AV Systems Mapless driving World Representations Beyond imitation learning Safety-aware end-to-end models Perception and Representation Learning Multi-modal sensor fusion 2D/3D detection, segmentation, depth estimation, scene understanding , neural representations Safe and Trustworthy Autonomous Systems Principled robustness Model explainability Control Barrier Functions (CBFs) Verification and Validation of Safety-Critical AI Systems Trustworthy AI/ML for autonomy and robotics Data-Strategies for AI Benchmarking AV

Nice To Haves

Experience with large-scale model training is a plus.

Responsibilities

Design and implement cutting-edge techniques in the field of vehicle autonomy.
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.