Objective of job 

Build and continuously optimise an industry‑leading Software Engineering Mgmt. System. for Mercedes-Benz RD China, enabling consistent, high‑quality, AI-empowered and scalable delivery of complex automotive projects, i.e., IVI/ADAS/TCU.

Task description (Main tasks)

1. Build an industry-leading software engineering management system with cost and efficiency governance

Act as a owner of the Software Engineering Management System.
Within the MB engineering context, benchmark against leading OEMs and Tier1s and establish a scalable, sustainable management system that enables large‑scale delivery, stable quality, predictable execution, and transparent control of engineering effort and cost.

2. Design an end-to-end software engineering lifecycle with planning anchors
Define an end‑to‑end software engineering lifecycle covering requirements, system and software design, implementation, integration, verification, release, and post‑release from a vehicle and system perspective.
Within this lifecycle, clearly establish phase boundaries, decision gates, handover logic, and inputs/outputs, addressing high HW/SW coupling and parallel multi‑configuration development, ensuring structured execution, clear accountability, and predictable cadence.

3. Establish unified requirements, governance framework, and end-to-end engineering controllability
Within the defined lifecycle framework, focus on the most critical and failure‑prone engineering areas by building unified mechanisms for requirements management (intake, analysis, decomposition, and change control), together with architecture, interface, and consistency governance.
Ensure decision documentation and full traceability, enabling cross‑team consistency, strong engineering controllability, and full lifecycle transparency.

4. Establish a vehicle software verification and release mgmt. system with lifecycle risk mgmt.
Within the lifecycle framework, establish a vehicle‑level software verification and release management system integrating quality and risk control into early development and integration phases.
Define risk‑based verification methodologies, control points, and release criteria aligned with requirements and architecture, ensuring controlled vehicle releases, SOP readiness, and balanced trade‑offs between quality, timing, and resources.

5. Continuously improve the process mgmt. system based on vehicle programs and project execution
Through execution across multiple vehicles and projects, systematically identify quality, efficiency, process, and cross‑team collaboration issues.
Use structured retrospectives and data‑based analysis to refine the management system, ensuring it continuously evolves with technology changes, organizational maturity, and business cadence.

6. Align projects, suppliers, and toolchains to enable process‑driven execution
Within the Software Engineering Management System, align internal projects, vehicle programs, and external partners through clearly defined interfaces, governance rules, and delivery expectations.
Drive consistent system adoption through toolchains and use of AI‑based capabilities, improving execution efficiency, deliverable quality, and early identification of integration, schedule, and budget risks.

Qualifications:

1. Bachelor degree or above, in Computer Science, Software Engineering, Electronics, Automation, Automotive Engineering, or related disciplines

2. 8–10+ years of work experience in vehicle software or complex system software development, in new Chinese OEMs, or Tier‑1 suppliers, with direct experience in building and operating software engineering systems for IVI/ADAS/TCU, rather than only executing predefined processes or supporting assessments.

3. Practical engineering experience with cross‑domain collaboration, tight hardware–software coupling, multi‑configuration parallel development, and system‑level integration. Experience working with software suppliers and Tier‑1 partners.

4. Ability to balance complex software engineering environments and make informed adaptation designs considering quality, efficiency, and delivery cadence.

5. Hands‑on experience with engineering and project management tools such as Jira, Azure DevOps, Confluence, and Feishu, with the ability to leverage AI tools to embed processes and quality controls into tool‑driven execution and documentation.

6. Fluent in English, good communication skills and eager to learn, proactive in work