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Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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Research Intern, World Models and Synthetic Data for Autonomous Driving - Summer 2027 will join the Autonomous Vehicle Applied Research team to advance safe autonomy through generative world models and synthetic data. The intern will research and prototype generative world models, traffic world models, and synthetic-data generation methods to capture multi-agent interactions and reactive behaviors. Key responsibilities include developing controllable generation for safety-critical driving scenarios and exploring how generative video models, multimodal foundation models, and self-supervised learning improve training-data quality. The role requires proficiency in Python, modern deep learning frameworks, 3D computer vision, and robotics. The work focuses on using generative models to create diverse, long-tail driving experiences to improve the robustness and generalization of autonomous systems while contributing to high-impact research publications.
Research and prototype generative world models and synthetic data generation methods for autonomous driving.
Develop traffic world models that capture multi-agent interactions and realistic, reactive behaviors.
Create methods for the controllable generation of diverse, interactive, and safety-critical driving scenarios.
Explore how generative video models and multimodal foundation models can improve training-data quality and coverage.
Design and conduct experiments to evaluate how generated data affects AV model performance and robustness.
Translate recent advances in generative modeling into practical approaches for autonomous driving systems.
Contribute to high-impact research publications and open research efforts.
What we're looking for
Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical/Computer Engineering, Robotics, or a related field.
Research experience in deep learning, computer vision, generative modeling, multimodal learning, robotics, or autonomous driving.
Strong foundations in machine learning and deep learning with hands-on experience developing and evaluating neural-network models.
Experience in generative models, video generation, multimodal learning, 3D computer vision, autonomous driving, or robotics.
Strong mathematical and analytical skills with an interest in designing rigorous experiments.
Strong Python programming skills and experience with modern deep learning frameworks.
Publications or research projects at conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues (preferred).
Hands-on experience with generative world models, video generation, diffusion models, autoregressive models, multimodal foundation models, or synthetic-data generation (preferred).
Generative AI
PyTorch
Python
Multimodal Learning
Reinforcement Learning
Computer Vision
Distributed Training
World Models
Robotics
Simulation
Video Understanding
VLMs
Foundation Models
Action-conditioned Simulation
Policy Learning
Robotics
Autonomous Vehicles
Physical AI
Deep Learning
Reinforcement Learning
Python
PyTorch
C++
CUDA
Parallel Programming
Simulation
Embodied AI
Machine Learning
Verification and Validation
Closed-loop Training
Computer Vision
Deep Learning
Generative AI
Python
C++
CUDA
PyTorch
Reinforcement Learning
VLMs
Parallel Programming
Git
Video Understanding
Simulation
Reconstruction
C++
CUDA
Python
C
ROS
PyTorch
TensorFlow
Keras
Linux
Computer Vision
SLAM
Deep Learning
OpenGL
LiDAR
Cloud Technologies
Version Control
Operating Systems
Data Structures
Physics Simulation
Image Processing