About our group:

The Data Analytics (DA) team within the Characterization & Analytics Group develops data pipelines, statistical methods, machine-learning models and AI-enabled engineering tools for Recording Head Technology R&D. We partner with engineers and subject-matter experts across Thailand and the global RHT organization to turn complex laboratory, image and parametric data into reproducible analysis, faster learning cycles and practical engineering decisions.

About the role - you will:

• Own a well-defined summer project that applies data analytics, computer vision or machine learning to a real R&D engineering problem, with clear objectives, measurable results and a final technical presentation.
• Build end-to-end analytics workflows: understand the engineering need, prepare and qualify data, develop a baseline, run controlled experiments, evaluate results and package the solution for handoff.
• Develop and evaluate image-based defect-detection or classification workflows using Python and modern AI/ML methods. Depending on project needs, this may include object detection, image tiling, data augmentation, model ensembling and post-processing.
• Work with engineering subject-matter experts to review labels, class definitions, edge cases, false positives and false negatives, then translate feedback into targeted model or workflow improvements.
• Create reproducible project assets, including source code, data-preparation steps, experiment configurations, model checkpoints, evaluation outputs and technical documentation.
• Use shared GPU computing resources and standard development practices to train, test and validate models, then collaborate with application or analytics teams on integration and deployment readiness.
• Communicate progress through concise updates, visual reviews and milestone demonstrations; explain both the technical approach and the engineering value to cross-functional audiences.

About you:

• Currently pursuing a bachelor’s, master’s or PhD degree in Computer Science, Data Science, Artificial Intelligence, Robotics, Electrical Engineering, Mechanical Engineering, Physics or a related technical field.
• Curious about applying AI and data methods to physical engineering and manufacturing problems, not only benchmark or classroom datasets.
• Able to break an open-ended problem into testable steps, learn unfamiliar domain terminology quickly and work independently with regular technical guidance.
• Comfortable collaborating with engineers, data scientists and application developers across functions and locations.
• Knowledge of Python, SQL, Machine Learning, Computer Vision, and data analysis.
• Able to communicate technical work in English through clear code comments, documentation, presentations and project discussions.

Your experience includes:

• Python for data and model development, including pandas and NumPy; experience with PyTorch or TensorFlow2 is strongly preferred.
• Data preparation and quality checks for CSV, tabular or image datasets, including missing-data review, class mapping, annotation validation and train/validation/test separation.
• Applied machine learning or deep learning, such as regression, classification, clustering, feature importance, convolutional neural networks or object detection.
• Model evaluation using appropriate metrics, such as precision, recall, F1 score, confusion matrices or error analysis, with attention to reproducibility and data leakage.
• SQL or relational database fundamentals for querying and organizing engineering data.
• Documenting a technical project so another person can reproduce the environment, training, inference and results.

You might also have:

• Hands-on experience with computer vision frameworks or object-detection models such as YOLO, YOLOX, Detectron2 or similar architectures.
• Experience with manufacturing, semiconductor, HDD, microscopy, AFM or SEM image data, including defect inspection or small-object detection.
• Experience with controlled ML experimentation, k-fold cross-validation, class imbalance, targeted data augmentation, model ensembles or class-specific threshold tuning.
• Familiarity with Linux, Jupyter, CUDA/GPU environments, Git, Docker or CI/CD workflows.
• Experience building a simple web application, dashboard or review tool that helps users inspect model predictions and provide feedback.

Location:

Location: Korat, Thailand

Our Korat facility is our largest manufacturing site worldwide, with more than 10,000 employees. Here, our team produces cloud-based storage equipment, from individual storage components to finished drives. Employees enjoy ample on-site parking, free shuttle vans and buses within the city of Korat, as well as transportation to and from Bangkok and Khon Kaen province on weekends. At work, you can grab a delicious breakfast, lunch, dinner, or snacks at one of our 24/7 on-site canteens or coffee shops. We offer table tennis, basketball, volleyball, a running/walking trail, and exercise classes in our on-site fitness center. In addition, there are relaxing spaces with comfortable seating both indoors and outdoors, perfect for taking a break during the day. Additionally, consider joining any of our employee resource groups (ERGs), such as Seagate Women in Leadership Network (SWLN), Seagate PRIDE: Thailand chapter, or Seagate Asia’s parents and caregivers group. We also have many on-site recreation events and opportunities to give back to the local community. 

Location: Korat Thailand
Travel: Up to 10%