智能驾驶数据科学家_CR

Company Description Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. Job Description • 研发面向自动驾驶、以数据为中心的算法，重点包括多模态数据（camera, lidar, radar）上的 auto annotation、auto tagging、data mining 和 auto quality check。 • 利用 Foundation Models、VLM、few-shot learning 和 zero-shot learning 来开发和优化数据自动化流水线，以提高效率和可扩展性。 • 实施 active learning 策略，进行智能数据选择和标注优先级排序。 • 与感知、预测和规划团队协作，理解数据需求并提供可扩展的数据解决方案。 • 与全球博世团队合作，进行技术转移、趋势追踪和方案评估。 • Research and development of data-centric algorithms for autonomous driving, focusing on auto annotation, auto tagging, data mining, and auto quality check across multi-modal data (camera, lidar, radar). • Develop and optimize data automation pipelines leveraging Foundation Models, VLM, few-shot learning, and zero-shot learning to improve efficiency and scalability. • Implement active learning strategies for intelligent data selection and annotation prioritization. • Collaborate with perception, prediction, and planning teams to understand data requirements and deliver scalable data solutions. • Work with global Bosch units on technology transfer, trend scouting, and concept evaluation. Qualifications • 计算机科学、电气工程、数据科学或相关专业的硕士或博士学位。 • 拥有1-3年在自动驾驶或AI应用领域担任以数据为中心角色的实践经验。 • 精通 Foundation Models (VLM, e.g., CLIP, Grounded-SAM, Grounded-DINO)、few-shot/zero-shot learning 和 active learning。 • 具备 auto annotation、data mining 和 auto quality check 流水线的实践经验。 • 熟练掌握 Python 及 PyTorch 或 TensorFlow 等深度学习框架。 • 熟悉多模态传感器数据（cameras, lidar, radar）。 • 在顶级会议（如 CVPR、ICCV、ECCV）以第一作者身份发表论文者优先。 • 加分项：具有使用 large feed-forward models 完成 3D reconstruction、depth estimation 或预测 camera intrinsic/extrinsic matrix 等任务的经验。 • 英语流利，具备强大的沟通和团队合作能力。 • Master’s or PhD in Computer Science, Electrical Engineering, Data Science, or a related field. • 1-3 years of practical experience in data-centric roles within autonomous driving or AI applications. • Strong knowledge of Foundation Models (VLM, e.g., CLIP, Grounded-SAM, Grounded-DINO), few-shot/zero-shot learning, and active learning. • Hands-on experience with auto annotation, data mining, and auto quality check pipelines. • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. • Familiarity with multi-modal sensor data (cameras, lidar, radar). • First-author publication at top conferences (e.g., CVPR, ICCV, ECCV) is a strong plus. • Bonus: Experience with tasks such as 3D reconstruction, depth estimation, or predicting camera intrinsic/extrinsic matrix using large feed-forward models. • Fluent in English, with strong communication and teamwork skills.