Reinforcement Learning and World Model for Autonomous Simulation Intern - 2027

Overview

• NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years.
• It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.
• Today, we’re tapping into the unlimited potential of AI to define the next era of computing.
• An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.
• Doing what’s never been done before takes vision, innovation, and the world’s best talent.
• As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work.
• Come join the team and see how you can make a lasting impact on the world.
• We are in search of 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.
• This is a great opportunity to shape the next frontier of intelligent driving, where imagination meets real-world impact.
• If you’re excited by the idea of building SOTA simulation techs and systems that learn, adapt, and truly “think,” we’d love to have you on board.
• Join us, join a team where your input plays a crucial role in fast-tracking the growth of autonomous vehicles with the state of art solutions.
• What you'll be doing: 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).
• What we need to see: 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.