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.

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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.

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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.

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What you will be doing:

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• 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.

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What we need to see:

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• 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.

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

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Potential internships require research experience in at least one of the following areas:

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Next-Generation AV Architectures

• Chain-of-Thought Reasoning

• Mixture-of-Experts

• Diffusion-LLMs

• Diffusion-based Trajectory Decoding

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Novel Policy Training Strategies

• Closed-loop Training

• Off-policy RL

• Online RL

• Enforcing Consistency

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Foundation and Multimodal Models

• Vision-language models

• Multimodal reasoning

• Spatial Multimodal Models

• Modality Alignment

• Model Scaling

• Synthetic data

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Inference Efficiency

• Inference Optimizations (e.g., parallel decoding, speculative decoding)

• Token Representations

• Model Distillation

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Simulation and Behavior Modeling

• Digital Twins

• Scenario Generation

• World Models

• Behavior/Traffic Modeling

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End-to-End AV Systems

• Mapless driving

• World Representations

• Beyond imitation learning

• Safety-aware end-to-end models

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Perception and Representation Learning

• Multi-modal sensor fusion

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

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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

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Data-Strategies for AI

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Benchmarking AV

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