Senior Generative ML & World Modeling Engineer

XPENG; Santa Clara (CA); On-site; USD 175,000 - 296,000; Full time

Job summary

XPENG is a leading smart-technology company driving innovation in AI, autonomous driving, EVs, eVTOLs, and robotics. We seek a Machine Learning Engineer with deep expertise in generative models and large-scale deep learning systems to research, implement, and evaluate world models learning dynamics from multimodal data.

You will work with diffusion/flow-matching models, video tokenizers, and transformer backbones on a world-class team.

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.
• 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.

Responsibilities

• 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.
• Define and build the evaluation methodology for predictive world models, spanning representation quality, prediction accuracy, generation fidelity, physical plausibility, long-horizon rollout consistency, and ultimately closed-loop policy performance, then feed the resulting models back into training as a source of synthetic data and corner-case simulation.

Skills

PyTorch

Distributed training

Python

Deep Learning

Research

Education

MS or PhD in CS/Engineering

Tools

FSDP

DeepSpeed

Megatron

Job description

XPENG is a leading smart-technology company driving innovation in AI, autonomous driving, EVs, eVTOLs, and robotics. We seek a Machine Learning Engineer with deep expertise in generative models and large-scale deep learning systems to research, implement, and evaluate world models learning dynamics from multimodal data.

You will work with diffusion/flow-matching models, video tokenizers, and transformer backbones on a world-class team.