Summer 2026 Intern - Data & AI Engineering (Head Development)

Company Description

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.

Job Description

About the Role

We are seeking a highly motivated Data & AI Engineering Intern to join our Head Development (HD) organization. This role focuses on building next-generation AI/ML capabilities that accelerate design learning, reduce technical risk, and extract insights from complex, multi-modal engineering data.

You will work at the intersection of:

• Machine learning (image analytics, anomaly detection, classification)

• Engineering data systems (parametrics, test data, process metadata)

• AI agents and decision-support tools

This is a hands-on role where you will design and implement intelligent systems that connect image-based defect signals with electrical and design data to generate actionable engineering insights.

Key Responsibilities

• Develop and deploy ML-driven pipelines that integrate:

  • Image-based defect detection/classification outputs

  • Electrical test and parametric data

  • Design and process metadata

• Build intelligent agents or analytical tools that:

  • Identify relationships between defects and performance

  • Detect emerging failure modes using multi-modal data

  • Generate hypotheses to accelerate root cause analysis

• Design data models and workflows to support scalable analytics across experiments and design iterations

• Collaborate with engineers and data scientists to translate domain problems into ML/AI solutions

• Present findings, insights, and prototypes to technical stakeholders

Example Project Areas

• Multi-modal defect → performance attribution models

• Failure mode discovery using unsupervised learning

• Design feature sensitivity analysis for defect susceptibility

• AI-assisted experiment learning and DOE optimization

• Knowledge capture and query systems for engineering insights

Qualifications

You may be a great fit if you are currently pursuing or recently completed a Master’s degree in Computer Science or a closely related field and have experience with some of the following skills or areas. You do not need to meet every qualification to apply.

• Strong Python programming skills, with experience building data-driven tools, models, or applications

• Understanding of machine learning fundamentals, such as classification, clustering, model training, and evaluation

• Experience working with data analysis or data pipelines using tools such as Pandas, NumPy, Dataiku, or similar technologies

• Familiarity with software engineering best practices, including writing maintainable code, debugging, and version control

• Experience or coursework related to computer vision, such as convolutional neural networks (CNNs) or image embeddings

• Exposure to anomaly detection, unsupervised learning, or classification models, including defect detection or quality analysis

• Experience working with multiple data sources or multi-modal datasets

• Experience building AI agents or applications using large language models (LLMs)

• Understanding of experimental design, model evaluation, or statistical analysis

• Interest in working with manufacturing, semiconductor, or hardware-related datasets

• Curiosity and strong