Durability CAE Engineer

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.

Key Technical Skills & Qualifications

• Simulation Software Expertise:
  Mastery of CAE tools such as Abaqus, Nastran and fatigue solvers like nCode Design Life 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 Hyper Mesh and Post-processors Hyper View 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.