Packaging Thermal and Mechanical Analysis Internship

About the Role

Join Lightmatter’s Packaging team for an internship focused on building the foundation of an AI-enabled framework for exploring package design options. This role combines physics-based simulation with machine learning, including physics-informed neural networks (PINNs) and other surrogate models, to generate fast, physically grounded predictions. Use cases include narrowing thermal solutions and predicting warpage, with the potential to extend to other objectives. Your work will directly influence upcoming products and build fundamental understanding of coupled thermal-structural-optical physics.

Key Responsibilities

• Build a flexible framework linking package design parameters (geometry, materials, process, thermal solution) to outputs like thermal performance and warpage.
• Develop and evaluate PINNs, other surrogate models, and reduced-order or analytical models for fast first-order predictions on new package concepts.
• Automate FEA and CFD setup, execution, and post-processing in Python to generate parametric datasets.
• Train and validate models against high-fidelity simulation and hardware data, quantifying accuracy and uncertainty.
• Plan and run physical DOEs (test setup, measurement, analysis) to build understanding of coupled thermal-structural-optical physics.
• Partner with packaging, thermal, and thermo-mechanical engineers to define requirements and adapt the framework for upcoming products.

Requirements & Tech Stack

• MS or PhD in Mechanical Engineering or a related field (currently pursuing).
• Coursework in heat transfer, fluid mechanics, FEA, CFD, numerical methods, and solid or fracture mechanics.
• Graduate research in multi-physics problems with both modeling and experimental validation.
• Experience with thermal simulation tools (Icepak, Fluent, Flotherm) and structural simulation tools (Ansys, Abaqus, COMSOL).
• Proficiency in Python for scripting, data processing, and simulation automation.
• Curiosity and willingness to learn.

Preferred Qualifications

• Graduate research in electronic or semiconductor packaging.
• Familiarity with thermal-structural coupling, warpage, and package-level thermal management.
• Coursework in packaging, numerical methods, or machine learning.
• Experience with PINNs, scientific ML, or surrogate modeling of FEA/CFD data using PyTorch, TensorFlow, or JAX.
• Interest in photonics and co-packaged optics.

Compensation

• Salary Range: $53 USD - $57 USD

Note: Total compensation includes base salary, new hire equity grant, annual performance-based equity, and other rewards. Benefits eligibility may vary.