Senior Machine Learning Engineer - Foundation 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. 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...