Staff Machine Learning Engineer - Foundation Model

About the role

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

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Design and implement large-scale multi-modal architectures (e.g., vision–language–action transformers) for end-to-end autonomous driving.

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Develop pretraining and fine-tuning strategies leveraging massive labeled and unlabeled fleet data (images, video, LiDAR, CAN bus, maps, human driving behaviors, etc.).

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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.

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Collaborate with infrastructure engineers to scale training across thousands of GPUs using distributed training frameworks (FSDP, DDP, etc.).

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Conduct systematic ablation, evaluation, and visualization of model behavior across perception, reasoning, and planning tasks.

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Contribute to model deployment optimization, including quantization, export, and latency–accuracy trade-offs for onboard execution.

Minimum Qualifications

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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.

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Strong proficiency in PyTorch and modern transformer-based model design.

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Experience in large-scale pretraining or multi-modal modeling (vision, language, or planning).

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Deep understanding of representation learning, temporal modeling, and self-supervised or reinforcement learning techniques.

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Familiarity with distributed training (DDP, FSDP) and large-batch optimization.

Preferred Qualifications

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PhD in CS/CE/EE or related field, with 1+ years of relevant industry experience.

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