Reinforcement Learning and World Model for Autonomous Simulation Intern - 2027

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…
• 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.

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