Autonomous Driving Data & Platform 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 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