Senior ML Systems Engineer - Vision & Anomaly Detection

Western Digital Corporation; Singapore; Hybrid; SGD 90,000 - 130,000; Full time

Job summary

Western Digital Corporation is seeking an ML Engineer in Singapore to own ML systems for defect detection, material behavior modeling with limited data, and active learning to select high-value experiments. You will run models in product development with immediate impact and collaborate across data engineers, software, and hardware teams.

The role emphasizes end-to-end ownership, deployment readiness, and documenting decisions for production handoff.

Qualifications

• Bachelor's or Master's degree in AI/ML/CS or related field; AI major or strong AI research preferred.
• 1–3 years of hands-on ML engineering experience with end-to-end ownership from training to deployment.
• Evidence of depth via Kaggle rankings, arXiv preprints, or notable open-source ML contributions.

Responsibilities

• Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement.
• Develop real-time anomaly detection for sensor and time-series data streams; own this stream.
• Own surrogate model pipelines and active learning systems under technical leadership.
• Define data contracts and validate datasets against model input requirements.
• Maintain ML models, training pipelines, and containers; contribute to CI/CD and monitoring.
• Provide code reviews and document model design decisions for production handoff.

Skills

Python

PyTorch

Computer Vision

Surrogate Modeling

Active Learning

Data-to-Model Interface

MLOps

Model Evaluation

Technical Documentation

Education

Bachelor's or Master's in AI/ML/CS

Tools

MLflow

Docker

Git

CI/CD

Job description

Western Digital Corporation is seeking an ML Engineer in Singapore to own ML systems for defect detection, material behavior modeling with limited data, and active learning to select high-value experiments. You will run models in product development with immediate impact and collaborate across data engineers, software, and hardware teams.

The role emphasizes end-to-end ownership, deployment readiness, and documenting decisions for production handoff.