AI-Driven Design Algorithm Engineer

1. Key Responsibilities
End-to-End Process Automation: Responsible for bridging the closed-loop workflow from 3D structural generation to physical simulation validation, establishing a fully automated and iterative "design-feedback-optimization" pipeline.
MCP & Skill Development/Integration: Extract and abstract engineering domain knowledge—such as Computer-Aided Design (CAD) workflows and electromagnetic simulation/testing experiences—to develop Model Context Protocol (MCP) tools or specialized skills for AI agents, integrating them into target functionalities.
Generative 3D Design Development: Utilize diverse methodologies, including end-to-end modeling and pre-training + fine-tuning paradigms, to perform structural topology optimization, exploring innovative architectural designs that satisfy strict signal integrity (SI) performance criteria.
Intelligent Modeling Engine: Drive advanced secondary development based on mainstream CAD suites (e.g., Creo/ProE, SolidWorks) or open-source 3D modeling frameworks (e.g., FreeCAD) to deliver parameter-driven generation and rapid optimization loops for complex structures.

2 Job Qualifications
Bachelor’s degree or above in Mechanical Engineering, Automation, Computer Graphics, or a closely related discipline.
Solid understanding and foundational background in Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE).
 Familiarity with standard machine learning algorithms, proficiency in Python, and hands-on experience in AI for CAD/CAE projects.
 Strong logical thinking, quick self-learning capabilities, and excellent collaborative communication skills.

3. Preferred Qualifications
Prior experience deploying AI models into actual industrial engineering workflows, such as fluid dynamics (CFD), structural mechanics, or electromagnetic (EM) simulations, is highly preferred.
Experience in Generative Design projects, particularly using Generative AI (GenAI) for engineering design optimization,