Job Responsibilities:

• Drive a focused research project on predictive world models, spanning problem formulation, architecture design, training, evaluation, and empirical analysis, in close collaboration with a mentor and the broader research team.
• Contribute to one or more of the following directions: high-quality multi-view future prediction and generation, supporting both action-conditioned rollouts and formulations that forecast the future without explicit action conditioning; architectures in which a shared backbone both predicts the future and produces trajectories or actions; predictive pre-training to improve Vision-Language-Action (VLA) driving performance.
• Extend prediction beyond 2D pixels into a shared multimodal latent space that spans 3D scene representations such as Gaussian Splatting, together with occupancy and reward signals, so that a single model can support simulation, evaluation, and policy training.
• Investigate cross-embodiment generalization through unified observation and action representations and embodiment-conditioning mechanisms, so that a single world model transfers across vehicles, robots, and sensor configurations with only few-shot data.
• Build evaluation methodology for predictive world models, spanning representation quality, prediction accuracy, generation fidelity, physical plausibility, long-horizon rollout consistency, and closed-loop policy performance.
• Collaborate with research engineers to move research prototypes into scalable training and inference pipelines, and publish and open-source results where appropriate.

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. Work under submission may be presented as an arXiv preprint.
• Strong, up-to-date foundation in generative modeling and experimental methodology, with hands-on experience building, training, fine-tuning, and evaluating models in PyTorch or JAX.
• Strong Python programming and software design skills, with a solid understanding of data structures, algorithms, code optimization, and large-scale data processing.
• Available to commit to a minimum of 12 weeks and work on-site at our Santa Clara office.

Preferred Skill Requirements:

• Hands-on experience with generative models for video or 3D, such as diffusion, flow matching, autoregressive video prediction, or neural scene representations including NeRF and Gaussian Splatting.
• Experience with world models or learned simulators for decision making, including model-based reinforcement learning and Vision-Language-Action (VLA) models.
• Experience with multimodal foundation models and video tokenizers or VAEs, including pretraining or adapting large pretrained backbones.
• Prior research internship experience in autonomous driving, robotics, or embodied AI, or contributions to widely used open-source projects.