端到端和VLA的岗位，三年经验月薪到70k了

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to 70k per month for positions requiring 3-5 years of experience [1] - The technology stack for end-to-end and VLA is complex, involving various advanced algorithms such as BEV perception, Vision-Language Models (VLM), diffusion models, reinforcement learning, and world models [1] - The industry is offering specialized courses to help individuals quickly and effectively learn about end-to-end and VLA technologies, featuring collaboration between academia and industry experts [1] Course Offerings - The "End-to-End and VLA Autonomous Driving Course" focuses on key algorithms and theoretical foundations in end-to-end autonomous driving, covering both one-stage and two-stage approaches, including BEV perception and large language models [11] - The "Autonomous Driving VLA and Large Model Practical Course" is designed for beginners in the VLA field, providing a comprehensive overview of VLA, including modules on Vision, Language, and Action, as well as reinforcement learning and diffusion models [2] - Both courses include practical assignments, allowing participants to build their own VLA models and datasets from scratch [2] Instructor Profiles - The course instructors include experts with strong academic backgrounds and practical experience in autonomous driving and large model development, such as those from Tsinghua University and top-tier universities [7][10][13] - Instructors have published numerous papers in prestigious conferences and have experience in leading projects related to multimodal perception and autonomous driving [7][10][13] Target Audience - The courses are aimed at individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and who have a grasp of concepts related to transformer models, reinforcement learning, and BEV perception [15] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [15]