Reinforcement Learning and World Model for Autonomous Driving Intern - 2026 at NVIDIA

Shanghai, China

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Autonomous Driving, AI, RoboticsIndustries

Requirements

Pursuing PhD in Computer Science, Machine Learning, or a related field, with neural rendering, robotics, or simulation background
Strong understanding of reinforcement learning (policy gradients, actor-critic, offline RL)
Familiarity with visual representation learning and 4D scene representation (NeRF, Gaussian Splatting, occupancy networks, contrastive, masked modeling, or generative world simulation) for world simulation
Experience building large-scale training pipelines with temporal consistency and simulation data replay
Publications or open-source contributions in RL, model-based control, or autonomous systems
Passion for developing learning systems that can “imagine” and plan in the real world

Responsibilities

Develop and refine multi-modal world models and integrate them into our simulation system
Train and evaluate self-supervised latent dynamics and sensor generation models for the joint tasks of trajectory prediction, goal-conditioned ego control, and sensor data synthesis
Explore and prototype hybrid architectures combining world models, generative (e.g., diffusion, flow matching) models, and policy gradients for realistic and robust simulation
Collaborate with End-to-End Driving Model teams to deploy world-model-based policies to simulated RL environments and accelerate the training of the driving systems
Contribute to system development for continuous learning and simulation adaptation (Sim2Real transfer)

Skills

Key technologies and capabilities for this role

Reinforcement LearningPolicy GradientsActor-CriticOffline RLWorld ModelsMulti-Modal SimulationNeural RenderingNeRFGaussian Splatting4D Scene RepresentationTrajectory PredictionDiffusion ModelsFlow MatchingSim2RealVisual Representation Learning

Questions & Answers

Common questions about this position

What education level is required for this internship?

Candidates must be pursuing a PhD in Computer Science, Machine Learning, or a related field, with a background in neural rendering, robotics, or simulation.

What key technical skills are needed for this role?

The role requires a strong understanding of reinforcement learning including policy gradients, actor-critic, and offline RL; familiarity with visual representation learning and 4D scene representation like NeRF, Gaussian Splatting, occupancy networks; and experience building large-scale training pipelines.

Is a salary mentioned for this internship?

This information is not specified in the job description.

What is the employment type for this position?

The position is full-time.

What makes a strong candidate for this internship?

Strong candidates will have publications or open-source contributions in RL, model-based control, or autonomous systems, along with a passion for developing learning systems that can imagine and plan in the real world.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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