Masters Intern – Machine Learning & Optimization for Industrial Engineering and Materials Science

Western Digital’s Big Data Technology & Industrial IoT team is seeking Masters-level interns passionate about applying Machine Learning, Deep Learning, and Optimization techniques to real-world problems in manufacturing, process control, and materials science. You will work on AI-driven models that enhance the reliability, yield, and sustainability of next-generation Hard Disk Drive (HDD) products — from materials characterization to factory optimization.

Key Responsibilities

• Design and implement predictive and optimization models for process yield, tool drift detection, and material performance using Deep Neural Networks, Gradient Boosting, or Graph Neural Networks.
• Apply Reinforcement Learning, Bayesian Optimization, or Evolutionary Algorithms to optimize process parameters, throughput, and energy consumption in high-volume manufacturing.
• Clean, preprocess, and augment large-scale sensor and experimental data. Interface with simulation tools (e.g., COMSOL, ANSYS) and integrate surrogate ML models for fast inference.
• Collaborate with data scientists, materials engineers, and process automation teams across global manufacturing sites to translate research into deployable solutions.

• Currently pursuing a Master’s degree in Machine Learning, Materials Science, Industrial Engineering, or related disciplines.
• Strong programming skills in Python, with experience in PyTorch, TensorFlow, Scikit-learn, Pandas, and Numpy.
• Knowledge of optimization algorithms, statistical modeling, and feature engineering.
• Exposure to time-series, image, or multi-modal data.
• Familiarity with Bayesian methods, Reinforcement Learning, or hybrid physics-informed ML is a plus.
• Strong analytical mindset with curiosity for bridging AI and physical sciences.

 

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