•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.

•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.