Autonomous Driving Data & Platform Engineer 自动驾驶数据闭环与平台开发工程师

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 Qualification 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 Specific knowledge Key Responsibilities
• Scenario Management &
  • Evaluation Infrastructure o Design and maintain the core scenario tree structure to support systematic scenario organization, hierarchical management, coverage tracking, and trend analysis. o Develop and optimize scenario-specific data statistics and visualization capabilities to improve scenario interpretability and management efficiency. o 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 o Foundation Components: Build core platform infrastructure including environment configuration, logging systems, SSO authentication, and other essential components. o 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. o 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 o 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. o 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. o Investigate and prototype AI-driven approaches for scenario mining, badcase analysis, and data selection to significantly boost overall R&D efficiency.

  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. 职位概述 负责自动驾驶数据闭环（Data Closed-loop）工具链的平台架构设计、工具链整合工程以及 AI 驱动工具开发。 该岗位重点围绕以下四大方向展开： -将分散的独立工具整合为统一工具链平台 -场景管理与评估基础设施建设 -自动驾驶研发 AI Agent 开发 -可扩展 Operator（算子）框架构建 理想候选人需要兼具： -扎实的平台级全栈开发能力 -架构设计经验 -对 AI/大语言模型（LLM）技术的浓厚兴趣 -自动驾驶数据工作流相关经验 任职资格 教育背景 硕士及以上学历，专业包括但不限于： 计算机科学 电气工程 机器人技术 汽车工程 或相关专业 工作经验 经验要求 5年以上自动驾驶、车载软件或仿真测试领域工作经验 至少3年以上 ADAS 工具链（Toolchain）开发经验