- Location
- US, CA, Santa Clara, United States of America
- Type
- Internship
- Department
- Education
- Seniority
- Internship
- Education
- PhD
- Source
- Workday
Description
By submitting your resume, you acknowledge that your Ph.D. Research Autonomous Vehicles internship application will be processed in accordance with NVIDIA’s Applicant Privacy Policy and you agree to our Terms of Service. 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.
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 Ph.D. degree in Computer Science, Electrical Engineering, or a related field, for the full duration of the internship; anticipated graduation date (month and year) must be clearly indicated on a resume or CV to be considered.
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.
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 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
Click here to learn more about NVIDIA, our early talent programs, benefits offered to students and other helpful student resources related to our latest technologies and endeavors.
You will also be eligible for Intern benefits.
Applications are accepted on an ongoing basis.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.