Hiring: Senior Machine Learning Engineer – Predictive World Model Company: XPENG Location: Santa Clara, CA Job Posted Time: 2026-09-12 03:28:52 Employment Type: Full-time Target Skills & Keywords : Computer Vision, Deep Learning, Machine Learning, Node.js, PyTorch, Python, R, Reinforcement Learning About the job Experience: •1-3 years + of experience working with DL frameworks such as PyTorch, including hands-on experience with distributed training (FSDP, DeepSpeed, or Megatron-style parallelism). •3 years + of experience working with DL frameworks such as PyTorch, including hands-on experience with distributed training (FSDP, DeepSpeed, or Megatron-style parallelism). Required Skills: •Research and develop predictive world models that learn how the physical world evolves, forecasting the future state of a scene from large-scale multimodal driving and robotics data. •Develop high-quality multi-view future prediction and generation, supporting both action-conditioned rollouts and formulations that forecast the future without explicit action conditioning. •Work at the boundary between world modeling and policy learning: develop architectures in which a shared backbone both predicts the future and produces trajectories or actions, and apply predictive pre-training to improve Vision-Language-Action (VLA) driving performance. •Extend prediction beyond 2D pixel 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. •Advance cross-embodiment generalization: design unified observation and action representations, together with embodiment-conditioning mechanisms, so that a single world model transfers across vehicles, robots, and sensor configurations with only few-shot data. Qualifications: •MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Computer Vision, Generative Models, or a related field, or equivalent experience. Open to recent graduates. •Strong experience in applied deep learning including model architecture design, large-scale model training, data curation, and empirical analysis. •1-3 years + of experience working with DL frameworks such as PyTorch, including hands-on experience with distributed training (FSDP, DeepSpeed, or Megatron-style parallelism). •Strong Python programming experience with software design skills. •Solid understanding of data structures, algorithms, code optimization and large-scale data processing. •Excellent problem-solving skills, including the ability to design controlled experiments and draw sound conclusions from noisy training signals. •Applied hands-on capability in generative models for video or 3D, such as diffusion, flow matching, autoregressive video prediction, or neural scene representations including NeRF and Gaussian Splatting. •Practical experience utilizing world models or learned simulators for decision making, including model-based reinforcement learning and Vision-Language-Action (VLA) models. •Practical experience utilizing multimodal foundation models and video tokenizers or VAEs, including pre-training or adapting large pre-trained backbones. •Practical experience utilizing large-scale training infrastructure and performance optimization, such as mixed precision, torch.compile, kernel-level optimization, and multi-node scaling. Compensation: •$174,720 - $295,680 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!