Autonomous Driving Data & Platform Engineer ID217444

该职位来源于猎聘 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 platform architecture design, consolidation engineering, and AI-powered tooling development of the autonomous driving data closed-loop toolchain. This role focuses on four pillars: building a unified toolchain platform from fragmented standalone tools, scenario management and evaluation infrastructure, AI agent development for autonomous driving R&D, and extensible operator framework construction. The ideal candidate combines strong full-stack platform engineering skills with a keen interest in applying AI/LLM technologies to autonomous driving data workflows Key Responsibilities Scenario Management & Evaluation Infrastructure

• Design and maintain the core scenario tree structure to support systematic scenario organization, hierarchical management, coverage tracking, and trend analysis.
• Develop and optimize scenario-specific data statistics and visualization capabilities to improve scenario interpretability and management efficiency.
• Build evaluation pipeline infrastructure including metric definition frameworks, automated report generation, and regression analysis tools to support data quality assessment across the closed-loop workflow. Toolchain Platform Consolidation
• Foundation Components: Build core platform infrastructure including environment configuration, logging systems, SSO authentication, and other essential components
• Capability Unification: Drive the consolidation of fragmented tool capabilities — data decoding, data health checks, data management and retrieval, coarse/fine data selection, ground-truth (GT) production, and data visualization — into a unified one-stop platform
• Platform Migration & Refactoring: Align with the overall platform roadmap to migrate, integrate, and re-architect existing tools, achieving toolchain standardization and platformization AI Agent & Intelligent Tooling for Autonomous Driving
• Independently design, develop, and iterate on purpose-built AI agent tools tailored to autonomous driving R&D workflows, including intelligent data quality assessment, automated evaluation metric development, and standardized report generation
• Explore deep integration of large language model capabilities with the data closed-loop toolchain, leveraging intelligent automation to optimize data production, evaluation, and operations end-to-end
• Investigate and prototype AI-driven approaches for scenario mining, badcase analysis, and data selection to significantly boost overall R&D efficiency Education Master degree in field such as Computer Science, Electrical Engineering, Robotics, Automotive Engineering or equivalent Experience
• 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 Technical Requirements
• Python Full-Stack Development: Proficient in Python-based backend development with strong command of API design, databases (relational and NoSQL such as MongoDB), caching (Redis), and search engines (Elasticsearch); capable of frontend development (JavaScript, HTML, modern frameworks such as Vue/React) to independently deliver end-to-end platform features
• Architecture & Framework Design: Demonstrated ability to design extensible frameworks or operator systems that support plugin-based or template-driven development; experience evolving a system through major architectural refactoring (e.g., v1 to v2 migration)
• AI/LLM Application Awareness: Familiarity with large language model capabilities and AI agent development concepts; able to evaluate and prototype AI-driven solutions for data processing, evaluation, or workflow automation scenarios
• Autonomous Driving Domain Knowledge: Familiarity with autonomous driving development workflows, including concepts such as scenario libraries, scenario tree structures, simulation-based testing, evaluation metrics, or data closed-loop pipelines
• Independent Delivery: Able to independently drive complex platform features from architecture design through development and launch with minimal supervision; strong ownership of deliverables and outcomes
• Cross-Role Communication: Able to engage with algorithm engineers and business users to translate domain-specific needs into robust, reusable platform capabilities
• Language: Working proficiency in English Preferred Qualifications
• Experience building scenario libraries, scenario tree structures, or evaluation/testing frameworks for autonomous driving systems
• Experience developing operator-based or plugin-based framework architectures that support extensible, template-driven development at scale
• Familiarity with simulation-based testing, evaluation metric design, or automated regression analysis for planning and control modules
• Hands-on experience in AI/LLM application development or intelligent agent tool implementation, particularly in evaluation or data quality domains
• Hands-on experience with data closed-loop systems, annotation platforms, or autonomous driving toolchains
• Familiarity with Docker, Kubernetes, and CI/CD pipelines for platform service deployment
• Familiarity with ROS, rosbag, and mainstream data formats in the autonomous driving industry