Staff Machine Learning Engineer

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.Job Responsibilities:Research and develop algorithms for reinforcement learning based methods for planning.Design efficient model architectures that can run in real-time on the computing platform of our vehicles.Develop offline data-driven ML infrastructure for fast adaptation of the planning ML models.Deliver on target planning SW and closely work with the perception team to achieve the most intelligent autonomous driving systems.Work with massive field-testing data to continuously improve autonomous driving technologies.Designing, running, and analyzing experiments and testing to evaluate the efficiency of our solutions on real-world data.Partnering with system software engineering specialists to ship industrial strength ML models.Communicating and collaborating with multi-functional teams.Minimum Skill Requirements:Education in Engineering, Robotics, Computer Science with a focus on Reinforcement Learning, Artificial Intelligence, or a related field, or equivalent experience.Strong experience in applied reinforcement learning including model architecture design, model training, data mining, and data analytics.3-5 + years of experience working with machine learning frameworks such as PyTorch, Tensorflow.Strong Python programming experience with software design skills.Solid understanding of data structures, algorithms, code optimization and large-scale data processing.Excellent problem-solving skills.Preferred Skill Requirements:MS or PhD level education in Engineering, Robotics or Computer Science with a focus on Reinforcement Learning, Artificial Intelligence, or a related field, or equivalent experience.Related experience in autonomous driving or robotics production solutions.Hands on experience in developing RL based planning engine for autonomous driving or robotic system.Experience in model deployment and optimization tools such as ONNX and TensorRT.What do we provideA 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 a significant impact on the transportation revolution by the means of advancing autonomous driving.Competitive compensation package.Snacks, lunches, dinners, and fun activities.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.