# Intern - Reinforcement Learning and Multi-Modal World Simulation Model

**Company**: NVIDIA
**Location**: Shanghai
**Experience**: internship
**Job type**: internship
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Shanghai/Reinforcement-Learning-and-World-Model-for-Autonomous-Simulation-Intern---2027_JR2028022?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_3253f3ed-bb4

## Description

NVIDIA is seeking a hardworking intern with expertise in Reinforcement Learning and Multi-Modal World Simulation Model to propel the evolution of ML-centric autonomous driving and Physical AI solutions.

The focus of this role lies in model-centric RL, learning about world simulation models, and translating state-of-the-art (SOTA) algorithms into real-world applications, allowing vehicles to interpret, anticipate, and respond astutely in challenging dynamic contexts.

**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).

**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 and 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.

## Skills

### Required
- Reinforcement Learning
- Multi-Modal World Simulation
- Machine Learning
- Computer Science
- Neural Rendering
- Robotics
- Simulation

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/China-Shanghai/Reinforcement-Learning-and-World-Model-for-Autonomous-Simulation-Intern---2027_JR2028022?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)