Human Interactive Driving Research Intern, World Models

Job description

Job responsibilities

• Conduct original research in one or more areas: world modeling, multi-agent interaction, reinforcement learning, perception, or sim-to-real transfer.
• Collaborate closely with full-time researchers on the design, training, and evaluation of learning-based driving systems.
• Develop and evaluate prototypes in closed-loop simulation environments and, time permitting, on high-performance autonomous driving hardware.
• Present research findings through internal talks and work towards a top-tier academic publication.
• Integrate and work with large-scale datasets (open-source and internal).

Job requirements

• Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related fields.
• Strong background in machine learning, particularly in areas such as deep learning, generative models, reinforcement learning, or probabilistic modeling.
• Hands-on experience training and evaluating world models, model-based reinforcement learning, 3D reconstruction or sensor fusion, and large-scale simulation for robotics or autonomous systems.
• Proficient coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow, etc.).
• Prior publication(s) in top-tier AI/ML conferences.
• Probabilistic and generative modeling.
• Autonomous driving datasets.
• Open-source foundation models.