1、 Key Responsibilities
l 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.
l 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.
l 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.
l 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
l Bachelor’s degree or above in Mechanical Engineering, Automation, Computer Graphics, or a closely related discipline.
l Solid understanding and foundational background in Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE).
l Familiarity with standard machine learning algorithms, proficiency in Python, and hands-on experience in AI for CAD/CAE projects.
l Strong logical thinking, quick self-learning capabilities, and excellent collaborative communication skills.
3. Preferred Qualifications
l Prior experience deploying AI models into actual industrial engineering workflows, such as fluid dynamics (CFD), structural mechanics, or electromagnetic (EM) simulations, is highly preferred.
l Experience in Generative Design projects, particularly using Generative AI (GenAI) for engineering design optimization, is a strong plus.
Work Location: 现场办公
