AI Research Intern - Predictive World Model

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. We are seeking PhD research interns with strong expertise in generative modeling and a demonstrated record of original research. In this role, you will work alongside our research team to develop world models that learn the dynamics of the physical world from large-scale multimodal data. ## Job Responsibilities: - Drive a focused research project on predictive world models, spanning problem formulation, architecture design, training, evaluation, and empirical analysis. - Contribute to one or more directions including multi-view future prediction, action-conditioned rollouts, and predictive pre-training for VLA driving. - Extend prediction beyond 2D pixels into a shared multimodal latent space that spans 3D scene representations such as Gaussian Splatting. - Investigate cross-embodiment generalization through unified observation and action representations. - Build evaluation methodology for predictive world models. - Collaborate with research engineers to move research prototypes into scalable training and inference pipelines. ## Minimum Skill Requirements: - Currently pursuing a PhD in Engineering, Computer Science, or a related field, with a focus on Deep Learning, Computer Vision, or Generative Models. - First-author publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, CoRL, RSS, or SIGGRAPH. - Strong foundation in generative modeling and experimental methodology with hands-on PyTorch or JAX experience. - Strong Python programming and software design skills. - Available to commit to a minimum of 12 weeks and work on-site at our Santa Clara office.