Durability CAE Engineer

Job Description Responsibilities Virtual Sign-off & Validation: Lead the structural durability and fatigue sign-off for critical systems like body structures, frames, and electric vehicle (EV) battery trays before physical prototype builds. Advanced Simulation Leadership: Oversee complex, full-vehicle explicit and implicit dynamic simulations (e.g., ground strikes, curb strikes, and cyclic loading) to assess structural resilience. Model Correlation: Drive the alignment between virtual simulation models and physical test results from proving grounds or lab rigs to ensure predictive accuracy. Root Cause Analysis: Use physics-based principles and simulation data to diagnose and resolve durability failures from early development through production. Technical Mentorship: Act as a "subject matter expert" (SME), coaching junior engineers and developing new CAE methodologies. Cross-functional Collaboration: Engage with design, manufacturing, and "Road Load" teams to develop design load targets and ensure lessons learned are integrated into future vehicle architectures. Requirements Key Technical Skills & Qualifications Simulation Software Expertise: Mastery of CAE tools such as Abaqus, Nastran and fatigue solvers like nCode DesignLife or FEMFAT. Material Science Knowledge: Deep understanding of fatigue life prediction, plasticity, ductile failure, and metal joining methods (e.g., welding in HSLA steels or cast materials). Data Processing: Proficiency in pre-processors ANSA or HyperMesh and Post-processors HyperView or Meta/Post. Familiarity in scripting languages like Python or MATLAB for automation would be a plus. Communication & Presentation: Excellent communication skills, both written and verbal, with a proven ability to translate complex data into clear technical and executive presentations for leadership decision-making Experience: Requires 10+ years of experience in structural components and CAE correlation for specialist roles. Special Considerations: AI & Machine Learning Proficiency in applying Reduced Order Modeling (ROM) and Neural Networks to accelerate traditional CAE simulations.

Created: 2026-03-04