NVIDIA 2027 Internships: Ph.D. Research Autonomous Vehicles

at Nvidia

USD 38-94 per hour

INTERN

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 3 CUDA @ 3 Communication @ 3 Deep Learning @ 3 LLM Machine Learning PyTorch @ 3 Python @ 3 Reinforcement Learning @ 3 Robotics @ 3 TensorFlow @ 3

Details

NVIDIA is seeking strategic, ambitious, hard-working, and creative Ph.D. students for internship opportunities with its Autonomous Vehicles teams. Interns will gain hands-on experience addressing challenges in vehicle autonomy, AI, robotics, and related technologies.

Applications are reviewed on an ongoing basis, and a recruiter may reach out if the applicant's experience matches an available internship opportunity. Applicants acknowledge that their application will be processed according to NVIDIA's Applicant Privacy Policy and Terms of Service.

Responsibilities

Design and implement cutting-edge techniques in the field of vehicle autonomy.
Collaborate with team members, other teams, and external researchers.
Transfer research to product groups to enable new products or product categories.
Deliver prototypes, patents, products, and/or original research publications.

Requirements

Actively enrolled in a university Ph.D. program in Computer Science, Electrical Engineering, or a related field for the full duration of the internship.
Clearly indicate the anticipated graduation month and year on the resume or CV.
Depending on the internship, prior experience or knowledge may include Python, C++, CUDA, and deep learning frameworks such as PyTorch and TensorFlow.
Strong research background 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, and diffusion-based trajectory decoding.
Novel Policy Training Strategies: Closed-loop training, off-policy reinforcement learning, online reinforcement learning, and consistency enforcement.
Foundation and Multimodal Models: Vision-language models, multimodal reasoning, spatial multimodal models, modality alignment, model scaling, and synthetic data.
Inference Efficiency: Inference optimizations such as parallel decoding and speculative decoding, token representations, and model distillation.
Simulation and Behavior Modeling: Digital twins, scenario generation, world models, and behavior or traffic modeling.
End-to-End AV Systems: Mapless driving, world representations, learning beyond imitation, and safety-aware end-to-end models.
Perception and Representation Learning: Multimodal sensor fusion; 2D/3D detection, segmentation, depth estimation, scene understanding, and neural representations.
Safe and Trustworthy Autonomous Systems: Principled robustness, model explainability, control barrier functions, verification and validation of safety-critical AI systems, and trustworthy AI/ML for autonomy and robotics.
Data Strategies for AI.
Benchmarking AV.

Benefits

Intern benefits are provided.
NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.
NVIDIA uses AI tools in its recruiting processes.

The internship hourly rate is based on the position, location, year in school, degree, and experience. The hourly rate for interns is USD 38–94.

This posting is for an existing vacancy.