AD Data Toolchain Engineer

该职位来源于猎聘 Description - External China is Mercedes-Benz’s largest passenger car market globally. At Mercedes-Benz R&D China, we are committed to delivering China-fit ADAS/AD solutions through cutting-edge technologies for our Chinese customers. We are now seeking talented professionals who share our passion and dedication to building advanced, China-oriented ADAS/AD systems. Responsible for the development, maintenance, and operational support of the autonomous driving data closed-loop toolchain, covering core data pipeline services, toolchain platform consolidation, and multi-cloud deployment operations — with a focus on cloud-based full-stack engineering to support the entire data lifecycle for autonomous driving development and operations. Key Responsibilities Data Toolchain Development & Maintenance: Own the development, operations, and continuous optimization of the autonomous driving data toolchain, responding swiftly to evolving business requirements. Core service areas include: Data Lifecycle Management Services: Maintain end-to-end pipeline data assets and provide standardized APIs covering bag management, joined-bag processing, geometric data management, ground-truth data management, project management, tagging, and topic management Data Selection & Annotation Platform: Operate and optimize the data annotation and fine-selection platform, enabling annotators to efficiently curate high-quality datasets for downstream consumption; maintain and iterate on the internal MB ground-truth annotation platform Cloud Deployment and Operations Manage service deployment, day-to-day operations, monitoring, troubleshooting, and issue resolution across multi-cloud environments (public cloud, compliance cloud, etc.) Establish and maintain alerting and incident response workflows to ensure service availability and compliance Requirements Management & Coordination Interface with business stakeholders to clarify requirements, align on solutions, and manage task breakdown, scheduling, and end-to-end progress tracking Ensure smooth delivery and stable post-launch operations Qualifications - External Education Master degree in field such as Computer Science, Electrical Engineering, Robotics, Automotive Engineering or equivalent Experience Experienced Candidates More than 5 years of experience in autonomous driving, vehicle software, or simulation domains Minimum 3 years of proven experience as developer focused on ADAS tool chain Fresh Graduates Bachelor's, Master’s or above in Computer Science, Automotive Engineering or relevant disciplines Keen interest in ADAS tool chain, autonomous driving and simulation technology Good logical thinking and learning ability Technical Requirements Backend Development & API Engineering: Proficient in at least one backend language (Python, Java, or Go) with strong command of RESTful API design, relational databases, NoSQL databases, and message queues; able to independently develop and maintain high-availability service interfaces Containerization & Cloud Operations: Hands-on experience with Docker and Kubernetes for containerized deployment; familiar with multi-cloud environments (public cloud, compliance cloud); capable of independently performing service deployment, monitoring, troubleshooting, and incident resolution in production Service Stability Mindset: Strong sense of ownership over production services; experienced in establishing or maintaining monitoring, alerting, and on-call workflows; able to respond rapidly to online incidents and drive root-cause analysis to closure Project Coordination & Multi-Tasking: Excellent task decomposition and progress control skills; able to independently manage multiple parallel requirements from intake through delivery, ensuring on-time release and stable operations Outstanding analytical thinking and problem-solving skills Excellent communication and cross-team collaboration abilities Proactive, results-driven working attitude Language: Working proficiency in English Preferred Qualifications Hands-on experience with autonomous driving data platforms, data closed-loop systems, or annotation platforms Proficiency in cloud-native technologies such as Kubernetes, Argo Workflows, and Helm, with production-grade operational experience Experience with observability stacks (Prometheus, Grafana, ELK, etc.) for service monitoring and log analysis Experience deploying and operating services across public cloud, compliance cloud, or other multi-cloud environments Familiarity with ROS, rosbag, and mainstream data formats and processing workflows in the autonomous driving industry