Core Viewpoint - The article discusses the differences between VLA (Vision-Language-Action) and end-to-end models in the context of autonomous driving, emphasizing the importance of large models and their applications in the industry [2]. Group 1: Job Descriptions and Requirements - Positions related to large model development, including VLA and end-to-end roles, are highlighted, with a focus on skills in fine-tuning, lightweight models, and deployment [2]. - The job of an end-to-end/VLA engineer involves developing and implementing driving systems, optimizing model structures, and constructing high-quality training datasets [6]. - The VLA/VLM algorithm position requires a master's degree in computer science or AI, with 3-5 years of experience in autonomous driving or AI algorithms, and proficiency in VLA/VLM architectures [8][10]. Group 2: Technical Skills and Experience - Candidates are expected to have experience with multimodal large language models, fine-tuning existing models for specific business scenarios, and familiarity with Transformer and multimodal technologies [5]. - Experience in computer vision, trajectory prediction, and decision planning is essential, along with a strong foundation in mainstream technologies and frameworks like PyTorch [9]. - The article emphasizes the need for candidates to have published papers in top conferences or achieved notable results in international competitions [9][11].