AD Data Toolchain Engineer

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

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