Job Summary:
We are seeking an experienced Applied Mechanician and Optical-Mechanical Engineer to join our team, working on the design and development of Meta's next-generation smart glasses and AR glasses. As a Display System Opto-ME, you will become part of a team translating new concepts through advanced mechanical simulation and fast iterative prototyping into products.
In this specific role, you will be responsible for combining Finite Element Analysis (FEA) and Machine Learning (ML) to drive reduced-degree mechanical performance prediction. By utilizing existing data sets from previous Meta glasses products, you will build surrogate models to accelerate structural risk assessment and design optimization. You will collaborate with cross-functional teams, including optical, mechanical, electrical, and software engineering teams, to ensure seamless integration and optimal performance of the display system.
Key Responsibilities:
• Develop and implement reduced-degree mechanical performance prediction models by combining FEA with machine learning techniques, leveraging historical datasets from previous Meta glasses products to predict failure modes and structural instability.
• Design and deploy neural network surrogate models (e.g., using PyTorch) integrated directly into FEA solvers (such as Abaqus subroutines) to simulate history-dependent material behaviors and multiscale structural responses efficiently.
• Guide the FEA team for mechanical simulation, structural risk assessment, and design optimization of the display system, specifically applying iterative operator learning to improve out-of-distribution data transferability for new form factors.
• Develop advanced constitutive models for complex materials used in display systems (including metals, polymers, adhesives, and brittle materials) to accurately capture nonlinearities, creep, and fracture mechanics under dynamic loading.
• Perform Bayesian statistical inference and uncertainty quantification to correlate simulation results with noisy experimental metrology data, bridging the gap between theoretical models and physical test results.
• Collaborate with design engineers to connect system PD and module designs with total stack tolerance analysis, ensuring that ML-driven predictions align with physical fit studies and manufacturing capabilities.
• Drive failure analysis activities by simulating damage evolution and crack propagation in quasi-brittle materials, supporting product scalability and reliability teams.
Requirements:
• Ph.D. or Master’s degree in Mechanical Engineering, Materials Science, Computational Mechanics, or a related field.
• Extensive experience in Finite Element Analysis (FEA) and solid mechanics, with deep expertise in nonlinear dynamics, large deformation, and contact mechanics using tools like Abaqus or ANSYS.
• Proven expertise in developing and applying Machine Learning (ML) models to mechanics problems, including experience with PyTorch, scikit-learn, and building neural network surrogate models for simulation.
• Proficiency in programming languages including Python, C++, Fortran, and MATLAB, specifically for writing user subroutines (e.g., VUMAT/UMAT) and integrating ML inference into FEA workflows.
• Experience with reduced-order modeling, operator learning, or LSTM/RNN architectures for analyzing history-dependent data and spatial-temporal fields.
• Working knowledge of material behaviors including plasticity, creep, and fracture mechanics for ductile and brittle materials.
• Ability to apply analytical and problem-solving skills to analyze complex opto-mechanical systems and identify innovative solutions for reduced-degree performance prediction.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams to translate complex simulation results into actionable design guidance.
Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation.
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