Job Opportunities API

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. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI,  machine learning , and smart connectivity.   We are looking for a full-time  Machine Learning Engineer  / Research Scientist  to drive the modeling and algorithmic development of XPENG’s next-generation  Vision-Language-Action (VLA) Foundation Model  — the core brain that powers our end-to-end autonomous driving systems. You will work closely with world-class researchers, perception and planning engineers, and infrastructure experts to design, train, and deploy large-scale multi-modal models that unify vision, language, and control. Your work will directly shape the intelligence that enables XPENG’s future L3/L4 autonomous driving products. Key Responsibilities Design and implement  large-scale multi-modal architectures  (e.g., vision–language–action transformers) for end-to-end autonomous driving. Develop  pretraining and fine-tuning strategies  leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.). Research and integrate  cross-modal alignment  (e.g., visual grounding, temporal reasoning, policy distillation, imitation and reinforcement learning) to improve model interpretability and action quality. Collaborate with infrastructure engineers to  scale training across thousands of GPUs  using distributed training frameworks (FSDP, DDP, etc.). Conduct  systematic ablation, evaluation, and visualization  of model behavior across perception, reasoning, and planning tasks. Contribute to  model deployment  optimization , including quantization, export, and latency–accuracy trade-offs for onboard execution. Minimum Qualifications Master’s degree or higher in  Computer Science, Electrical/Computer Engineering, or related field , with  3+ years of experience  in deep learning research or productization. Strong proficiency in  PyTorch  and modern transformer-based model design. Experience in  large-scale pretraining  or  multi-modal modeling  (vision, language, or planning). Deep understanding of  representation learning, temporal modeling , and  self-supervised or  reinforcement learning  techniques. Familiarity with  distributed training  (DDP, FSDP) and large-batch optimization. Preferred Qualifications PhD in  CS/CE/EE  or related field, with 1+ years of relevant industry experience. Publication record in top-tier AI conferences (CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV). Prior experience building  foundation or end-to-end driving models , or  LLM /VLM architectures  (e.g., ViT, Flamingo, BEVFormer, RT-2, or GRPO-style policies). Familiarity with  RLHF/DPO/GRPO ,  trajectory prediction , or  policy learning  for control tasks. Proven ability to collaborate cross-functionally with infra, perception, and planning teams to deliver production-ready models. What do we provide: A collaborative, research-driven environment with access to  massive real-world data  and  industry-scale compute. An opportunity to work with  top-tier researchers and engineers  advancing the frontier of foundation models for autonomous driving. Direct impact on the next generation of  intelligent mobility systems . Opportunity to make significant impact