Apple
Senior FEA Engineer
$185K–$325K
Cupertino, California · Senior · Full-time · Posted Sep 14, 2026
- Experience
- 10+ years required; preferred qualifications also state an advanced degree with 3 years of industry experience and 5+ years of advanced FEA experience in high-volume consumer electronics and/or modules.
- Education
- Bachelor's degree minimum in Mechanical Engineering, Solid mechanics of materials, or a related degree.
About the job
Leads structural, thermal, and dynamic FEA of display panels, modules, and integrated systems to optimize designs and investigate mechanical failures.
What you'll do
- Build and run mechanical models and static, dynamic, structural, and thermal-mechanical analyses; coordinate module models into system assemblies.
- Drive material characterization and testing, failure analysis, root-cause investigations, and design of experiments.
- Report results and develop engineering specifications and processes for consistent analysis and design validation.
What they ask for
- Bachelor's degree in Mechanical Engineering, Solid mechanics of materials, or a related degree.
- 10+ years of advanced FEA simulation experience in industrial settings.
- Experience with mechanics of materials, first-principles analysis, and static, dynamic, and thermal-mechanical simulations.
- Experience with material constitutive models for metals, brittles, plastics, and adhesives; FEA software experience with Abaqus (Implicit & Explicit) or ANSYS.
Nice to have
- Advanced related degree with 3 years of industry experience; 5+ years of advanced FEA in high-volume consumer electronics and/or modules, with flat-panel display experience preferred.
- Altair Hyperworks; nonlinear FEA; experience applying AI/ML to physics-based simulations or FEA optimization automation.
Pay
$184,700 and $324,800 (base salary)
Tools
Abaqus (Implicit & Explicit), ANSYS, Altair Hyperworks, AI/ML, surrogate modeling, reduced-order modeling (ROM)
AI focus
Preferred: apply AI/ML to physics-based simulations, including surrogate modeling, reduced-order modeling (ROM), data-driven material model calibration, and ML-driven optimization pipelines.
Model-written summary · the original posting is authoritative