Research Intern, World Models and Synthetic Data for Autonomous Driving - Summer 2027

NVIDIA Corporation (old); Santa Clara (CA); On-site; USD 55,104,000 - 202,507,000; Full time

Benefits offered by this job

Intern benefits

Job summary

NVIDIA Corporation's Autonomous Vehicle Applied Research team invites a Research Intern to advance generative world models and synthetic data for safe autonomy. You will work with researchers and engineers on scalable methods for training, evaluating, and improving AV systems.

You'll design and run large-scale experiments, contribute to publications, and translate recent advances into practical approaches for autonomous driving.

Qualifications

• Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical/Computer Engineering, Robotics, or a related field, with 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 and an interest in designing rigorous experiments.
• Strong Python programming skills and experience with modern deep learning frameworks.
• Self-motivated, collaborative, and comfortable working in a research environment with open-ended problems.
• Publications or research projects at conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues.

Responsibilities

• Research and prototype generative world models, traffic world models, and synthetic-data generation methods for autonomous driving.
• Develop traffic world models capturing multi-agent interactions and reactive behaviors for simulating complex driving scenarios.
• Develop methods for controllable generation of diverse, interactive, and safety-critical driving scenarios.
• Explore generative video models, multimodal foundation models, traffic simulation, data curation, active learning, self-supervised learning, and simulation to improve data quality and coverage.
• Design and conduct experiments to understand how generated data affects AV model performance, robustness, and generalization.
• Translate recent advances in generative modeling and machine learning into practical approaches for autonomous driving.
• Collaborate closely with researchers and engineers across NVIDIA on research prototypes and experimental systems.
• Contribute to high-impact research publications and open research efforts.

Skills

Python

Deep learning

Machine learning

Computer vision

Autonomous driving

Multimodal learning

Research proficiency

Education

MS or PhD in CS/EE/Robotics

Tools

PyTorch

TensorFlow

Job description

Be part of the Autonomous Vehicle (AV) Applied Research team and help advance safe autonomy through generative world models and synthetic data. This internship is an opportunity to work alongside a team of researchers and engineers developing the next generation of models that can understand, simulate, and reason about complex driving environments. We are looking for a passionate and highly motivated Research Intern with a strong background in machine learning and an interest in autonomous driving, generative world models, multimodal foundation models, and synthetic data generation. Our applied research team works closely with researchers and engineers across NVIDIA Research and the AV organization to develop scalable methods for training, evaluating, and improving autonomous systems. Our research spans generative video and world models, controllable scenario generation, multimodal learning, end-to-end driving, 3D computer vision, and data-centric learning. We are particularly interested in using generative models to create diverse, safety-critical, and long-tail driving experiences that can help improve the robustness and generalization of autonomous systems.

As a Research Intern, you will contribute to research on generative data engines for autonomous driving: developing and using world models to understand data coverage, generate targeted driving scenarios, and study how synthetic data can systematically improve AV models. You will have the opportunity to develop new research ideas, run large-scale experiments, collaborate with experienced researchers and engineers, and contribute to publications at top-tier conferences.

What you'll be doing:

• Research and prototype generative world models, traffic world models, and synthetic-data generation methods for autonomous driving.
• Develop traffic world models that capture multi-agent interactions and realistic, reactive behaviors, enabling simulation and generation of complex driving scenarios.
• Develop methods for controllable generation of diverse, interactive, and safety-critical driving scenarios.
• Explore how generative video models, multimodal foundation models, traffic simulation, data curation, active learning, self-supervised learning, and simulation can improve training-data quality, diversity, and coverage.
• Design and conduct experiments to understand how generated data affects AV model performance, robustness, and generalization.
• Translate recent advances in generative modeling and machine learning into practical approaches for autonomous driving.
• Collaborate closely with researchers and engineers across NVIDIA on research prototypes and experimental systems.
• Contribute to high-impact research publications and open research efforts.

What we need to see:

• Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical/Computer Engineering, Robotics, or a related field, with 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 one or more of the following areas: generative models, video generation, multimodal learning, 3D computer vision, autonomous driving, or robotics.
• Strong mathematical and analytical skills and an interest in designing rigorous experiments.
• Strong Python programming skills and experience with modern deep learning frameworks.
• Self-motivated, collaborative, and comfortable working in a research environment with open-ended problems.

Ways to stand out from the crowd:

• Publications or research projects at conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues.
• Hands-on experience with generative world models, video generation, diffusion models, autoregressive models, multimodal foundation models, or synthetic-data generation.
• Experience working with large-scale video, driving, robotics, or simulation datasets.
• Experience with autonomous driving, embodied AI, physical AI, 3D scene understanding, or end-to-end learning.
• Demonstrated ability to take a research idea from hypothesis ➜ implementation ➜ experimentation ➜ quantitative evaluation.

Benefits and Compensation

• Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package.
• You will also be eligible for Intern benefits.

As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience.

Your hourly rate for our interns is 20 USD - 71 USD.

Applications for this job will be accepted at least until October 13, 2026.

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.

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