About the role#
Join the Packaging team to build an AI-enabled framework that helps engineers explore package design options. You will combine physics-based simulation with machine learning to make fast, early predictions that remain physically grounded. Your work will influence upcoming products and test vehicles, helping the team narrow thermal solutions and predict warpage. You will work alongside experienced thermal and thermo-mechanical engineers to understand how package architecture, materials, and cooling integrate into AI and HPC products.
What you'll do#
- Build a flexible framework that links package design parameters to thermal performance and warpage outcomes.
- Develop and evaluate PINNs, surrogate models, and reduced-order or analytical models for fast first-order predictions.
- Automate FEA and CFD setup, execution, and post-processing using Python to generate parametric datasets.
- Train and validate models against high-fidelity simulation and hardware data while quantifying accuracy.
- Plan and run physical DOEs, including test setup, measurement, and analysis, to investigate coupled thermal-structural-optical physics.
- Partner with packaging and thermal engineers to define requirements and adapt the framework for new products.
What you'll need#
- Currently pursuing an MS or PhD in Mechanical Engineering or a related field.
- Coursework in heat transfer, fluid mechanics, FEA, CFD, numerical methods, and solid or fracture mechanics.
- Graduate research in multi-physics problems including both modeling and experimental validation.
- Experience with thermal simulation tools like Icepak, Fluent, or Flotherm.
- Experience with structural simulation tools such as Ansys, Abaqus, or COMSOL.
- Proficiency in Python for scripting, data processing, and simulation automation.
- Curiosity and a willingness to learn from the team.
- Preferred: Graduate research in electronic or semiconductor packaging.
- Preferred: Familiarity with thermal-structural coupling, warpage, and package-level thermal management.
- Preferred: Experience with PINNs, scientific ML, or surrogate modeling using PyTorch, TensorFlow, or JAX.
- Preferred: Interest in photonics and co-packaged optics.
Location & details#
- Location: Mountain View, California.
- Term: Summer 2027.
- Modality: Hybrid.
- Status: Full-time internship.
- Compensation: Paid position.
About Lightmatter
Lightmatter operates in the computer hardware manufacturing industry. The company builds technology designed to improve computer processing capabilities. It is a privately held organization founded in 2017. The business is based in Mountain View, California, and maintains a staff of 201 to 500 employees.
How to get in at Lightmatter
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