Autonomous Driving Data & Platform Engineer

China is Mercedes-Benz’s largest passenger car market globally. At Mercedes-Benz R&D China, we deliver China-fit ADAS/AD solutions through cutting-edge technologies, and we are seeking talented professionals who share this passion.

Responsible for building and evolving the AD data closed-loop toolchain that supports Mercedes-Benz China ADAS/AD mass-production programs and empowers algorithm and validation teams across R&D China. The role covers three pillars — scenario management and evaluation infrastructure, ADAS simulation and Component SiL support, and AI agent tooling — and grows alongside the industry shift toward end-to-end, data-driven autonomous driving. The ideal candidate is a self-driven engineer with a product mindset and strong interest in AI/LLM, who takes ownership from idea to adoption.

Key Responsibilities

Scenario Management & Evaluation Infrastructure

Scenario Tree: Design and maintain the scenario tree to support hierarchical organization, coverage tracking, and trend analysis.

DScenario Analytics: Develop scenario statistics and visualization to improve interpretability and management efficiency.

Evaluation Pipeline: Build evaluation infrastructure, including metric frameworks, automated reporting, and regression analysis, to assess algorithm performance and data quality and to support closed-loop evaluation of end-to-end models.

ADAS Simulation & Component SiL Support

Simulation Support: Assist in setting up and maintaining scenario-based and log re-simulation environments

Component SiL Integration: Help integrate ADAS components into the SiL environment and coordinate with algorithm teams on interface adaptation and issue troubleshooting

Test Automation: Contribute to automated simulation execution and regression testing, linking simulation results with the scenario library and evaluation pipeline

AI Agent & Intelligent Tooling for Autonomous Driving

AI Agent Tools: Design, develop, and iterate on AI agents for AD R&D workflows, such as data quality assessment, evaluation metric development, and report generation

LLM Integration: Drive LLM integration into the closed-loop toolchain to automate data production, evaluation, and operations end-to-end

AI-Driven Innovation: Propose and prototype AI-driven approaches for scenario mining, badcase analysis, and data selection to boost 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 software development experience, preferably in autonomous driving, vehicle software, or simulation domains

Minimum 2 years of hands-on experience in ADAS toolchain or AI application development

Technical Requirements

Python Development: Proficient in Python backend and data processing, including API design, databases (SQL/MongoDB), caching (Redis), and pandas

AD Domain Knowledge: Familiar with AD development workflows such as data closed-loop, scenario libraries, evaluation metrics, and simulation/SiL testing

C++ Proficiency: Able to read and debug C++ code for ADAS component integration in SiL environments

AI/LLM Application: Hands-on experience building LLM-based applications (e.g., RAG, tool calling, MCP, or agent frameworks such as LangGraph)

Language: Working proficiency in English

Mindset & Soft Skills

Ownership & Drive: Proactively identify pain points and drive solutions from design to launch without waiting for detailed specifications

Product Thinking: Treat internal tools as products — understand user needs, prioritize by impact, and iterate based on feedback

Independent Thinking: Challenge existing approaches and validate ideas quickly through MVPs

Cross-Role Communication: Translate needs from algorithm engineers and business users into reusable platform capabilities

Preferred Qualifications

Hands-on experience building scenario libraries, evaluation frameworks, or automated regression analysis for AD systems, including end-to-end models

Proven track record of shipping AI agent tools to real users in production, especially for evaluation or data quality

Experience with ADAS simulation tools (e.g., VTD, CarMaker, CARLA) and ROS/rosbag

Experience designing plugin/operator-based frameworks or leading major architectural refactoring

Familiarity with frontend (Vue/React), Elasticsearch, Docker/Kubernetes, and CI/CD