Autonomous Driving Data & Platform Engineer
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Before you apply
LocationMultiple Locations
Typefull-time
SenioritySenior
Experience5+ yrs
Company size10,000+ people
First seenOct 22, 2026 · 2w ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
Master degree in field such as Computer Science, Electrical Engineering, Robotics, Automotive Engineering or equivalent
More than 5 years of software development experience
Minimum 2 years of hands-on experience in ADAS toolchain or AI application development
Proficient in Python backend and data processing, including API design, databases (SQL/MongoDB), caching (Redis), and pandas
Familiar with AD development workflows such as data closed-loop, scenario libraries, evaluation metrics, and simulation/SiL testing
Able to read and debug C++ code for ADAS component integration in SiL environments
Hands-on experience building LLM-based applications (e.g., RAG, tool calling, MCP, or agent frameworks such as LangGraph)
Working proficiency in English
Ownership & Drive: Proactively identify pain points and drive solutions from design to launch
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
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
Skills
Python
SQL
MongoDB
Redis
pandas
C++
LLM
RAG
MCP
LangGraph
Vue
React
Elasticsearch
Docker
Kubernetes
CI/CD
VTD
CarMaker
CARLA
ROS
rosbag
About the company
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.
About the role
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
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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
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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