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

At Western Digital, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, Western Digital is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we’ve been doing just that—our technology helped people put a man on the moon and capture the first-ever picture of a black hole.

We offer an expansive portfolio of technologies, HDDs, and platforms for business, creative professionals, and consumers alike under our Western Digital®, WD®, and WD_BLACK™.

We are a key partner to some of the largest and highest-growth organizations in the world. From enabling systems to make cities safer and more connected, to powering the data centers behind many of the world’s biggest companies and hyperscale cloud providers, to meeting the massive and ever-growing data storage needs of the AI era, Western Digital is fueling a brighter, smarter future.

Today’s exceptional challenges require your unique skills. Together, we can build the future of data storage.

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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