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XPeng Motors in Santa Clara, CA is seeking a machine learning engineer to design, evaluate and deploy reinforcement learning based planning methods for autonomous driving. You will work with top experts in computer vision, AI systems and software to ensure world-leading performance on our fleet.
The role requires 3–5+ years in RL, strong Python and ML framework skills (PyTorch, TensorFlow), and experience building real-time models.
XPeng Motors is one of China’s leading smart electric vehicle (EV) companies. We design, develop, and manufacture 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 machine learning engineers with strong reinforcement learning design skills and software development skills. In this role, you will implement, evaluate and deploy reinforcement learning based methods for planning problems. You will be working with a team of the best-in-class computer vision, AI systems, and software engineers to ensure the world-leading performance on our autonomous vehicles. Your work will be supported by massive data from our autonomous fleet to deliver the best autonomous driving solution.
The base salary range for this full-time position is $179,400 - $303,600, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. 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.
We are an Equal Opportunity Employer. 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.