Staff Machine Learning Engineer - Foundation Model (Santa Clara)

XPeng Motors is one of Chinas leading smart electric vehicle (EV) companies. We design, develop, manufacture and market smart EVs that are seamlessly integrated with advanced Internet, AI and autonomous driving technologies. We are committed to in-house R&D and intelligent manufacturing to create a better mobility experience for our customers. We strive to transform smart electric vehicles with technology and data, shaping the mobility experience of the future. We are looking for a full-time Machine Learning Engineer, 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. 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. Knowledge of model training framework (e.g. 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. PhD in CS/CE/EE, or equivalent, with 3 + years of industry experience. # Strong publications records in top academic conferences or journals, e.g. Experience in training large scale vision or language models # Opportunity to work on cutting edge technologies with the top talents in the field. # The base salary range for this full-time position is $215,280-$364,320, in addition to bonus, equity and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations. #