Machine Learning Engineer - AI Foundation

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 - AI Foundation, with deep knowledge and strong enthusiasm towards establishing a state-of-art ML infrastructure for training very large foundation model and accelerating model training/inference. Our mission is to solve the autonomous driving problem. You will work with a team of talented software engineers, machine learning engineers and research scientists to push the boundary of state-of-art machine learning models which will enable the next-generation E2E solution of autonomous driving. Job Responsibilities: Design, train, and deploy large deep learning models that can leverage the vast amount of labeled and unlabeled data from a fleet of million vehicles. Minimum Skill Requirements: Master in CS/CE/EE, or equivalent, in industry experience. Good knowledge of PyTorch. Knowledge of model training framework (e.g. PyTorch Lightning). Knowledge of transformer architecture and ways to accelerate the training and inference of transformer models. Experience of using pytorch ddp for distributed training of models. Preferred Skill Requirements: Previous experience in the autonomous driving industry. Knowledge of Torchscript and Nvidia TensorRT. Being efficiently in solving complex problems collaboratively on larger teams. What do we provide: A fun, supportive and engaging environment. Infrastructures and computational resources to support your work. Opportunity to work on cutting edge technologies with the top talents in the field. Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving. Competitive compensation package. Snacks, lunches, dinners, and fun activities.