NVIDIA 2026 Internships: PhD Autonomous Vehicles Research - US

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’reseeking 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. 

What you will be doing: 

• 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. 

What we need to see: 

• 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 couldinclude 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. 

• Experience with large-scale model training is a plus. 

Potential internships 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 TrustworthyAutonomous 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 

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