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Nvidia NVIDIA Internships PhD Autonomous Vehicles Research 
United States, California 
690109470

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US, CA, Santa Clara
time type
Full time
posted on
Posted 6 Days Ago
job requisition id

your resume,expressing interest in one of our 2026Autonomous Vehiclesreview resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.

society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

with one of our industry leadingAutonomous Vehicles

Learn more about

What you will be doing:

  • Design and implementcutting-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.

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

Foundation and Multimodal Models

  • Vision-language models

  • Multimodal reasoning

  • Spatial Multimodal Models

  • Modality Alignment

  • Model Scaling

  • Synthetic data

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

  • driving

  • World Representations

  • Beyond imitation learning

  • Safety-aware end-to-end models

and Representation Learning

  • Multi-modal sensor fusion

  • 2D/3D detection, segmentation, depth estimation, scene understanding,neural representations

Autonomous Systems

  • Principled robustness

  • Model explainability

  • Control Barrier Functions (CBFs)

  • Verification and Validation of Safety-Critical AI Systems

  • AI/ML for autonomy and robotics

Data-Strategies for AI

Benchmarking AV

and other helpful student resources related to our latest technologies and endeavors.

You will also be eligible for Intern

Applications are accepted on an ongoingbasis.

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