AI/ML Process Engineering Intern

Role Overview

The AI/ML Process Engineering Intern will build machine-learning tools for perovskite process development and quality control, including computer vision defect detection and Gaussian Process-based recipe optimization. You will present project accomplishments to leadership upon completion.

Responsibilities

• Build machine-learning tools supporting perovskite process development and quality control alongside an engineer mentor with access to imaging tools and data libraries.
• Major Project:
• Months 1–3 (AI-Based Defect Detection): Train a transfer-learning computer vision model to identify defects such as pinholes and dendrites in perovskite device images, delivering a prototype that distinguishes at least two defect types.
• Months 4–6 (Recipe Optimization): Build a Gaussian Process surrogate model using Ax/BoTorch to propose next experiments for precursor ratios, annealing profiles, and coating parameters. Deliver a reusable closed-loop optimization framework replacing grid or one-factor-at-a-time search methods. Extend the defect library and layer statistical process control frameworks as time allows.
• Help GMO catch process defects earlier and shorten recipe-development cycles by presenting project summaries to leadership upon completion.