智能驾驶数据科学家_CR
Bosch Group • Shanghai, Shanghai, China
30 天前
职位描述
- 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 onauto annotation,auto tagging,data mining, andauto quality checkacross multi-modal data (camera, lidar, radar). * Develop and optimize data automation pipelines leveragingFoundation Models,VLM,few-shot learning, andzero-shot learningto improve efficiency and scalability. * Implementactive learningstrategies 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 ofFoundation Models(VLM, e.g., CLIP, Grounded-SAM, Grounded-DINO),few-shot / zero-shot learning, andactive learning. * Hands-on experience withauto annotation,data mining, andauto quality checkpipelines. * Proficiency in Python and deep learning frameworks such asPyTorchor 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.