Principal Engineer, Machine Learning

WESTERN DIGITAL (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 60,000 - 110,000; Full time

Job summary

WESTERN DIGITAL (SINGAPORE) PTE. LTD. is seeking a hands-on ML engineer who will own end-to-end ML systems for product development defects detection, surrogate modeling, and active learning pipelines.

You will work directly on real product solutions, train CNN/U-Net/ViT models, set up real-time anomaly detection, and ensure data quality before training, with mentorship for juniors and documentation for production hand-off.

Qualifications

• Education: Bachelor's or Master's in AI/ML/CS with AI focus preferred.
• 1–3 years of hands-on ML engineering experience including end-to-end pipelines.
• Evidence via Kaggle, arXiv papers, or open-source contributions valued.

Responsibilities

• Own ML model development for product defects detection and inspection.
• Build real-time anomaly detection for sensor/time-series data streams.
• Develop surrogate models and active learning pipelines under guidance.
• Define data contracts and validate datasets for model training.
• Maintain MLflow, containers, and CI/CD for product pipelines.
• Mentor juniors and document design decisions for production hand-off.

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

Most ML engineering roles at large companies mean contributing to a platform team where your work disappears into a pipeline that fifty other engineers also touch. This role is different. You will be the primary owner of the ML systems that detect product development defects, model material behavior with limited data, and select the highest-value experiments from an active learning pipeline. Your models will run in product development. Your decisions will matter immediately.

Key Responsibilities

• Deep Learning Model Implementation & Product Development Ownership: Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement. Own model performance end-to-end - ablation studies, confidence calibration, product development performance monitoring.
• Anomaly Detection Systems: Build and maintain real-time anomaly detection for product development sensor and time-series data streams - statistical baseline, threshold calibration, drift alerting. Sole implementation owner for this workstream.
• Surrogate Modeling & Active Learning Operations: Own implementation and iteration of surrogate model pipelines and active learning systems under Technical lead's architectural direction. Configure acquisition functions; integrate with versioned feature sets.
• Data-to-Model Interface Ownership: Own the data contract between the Data Engineer and the ML model stack. Define feature specifications, validate datasets against model input requirements, and elevate data quality issues before they reach the training pipeline.
• MLOps Maintenance & Product Development Reliability: Maintain model versions, training pipelines, and containers under platform architecture. MLflow tracking, CI/CD contribution, product development monitoring, and degradation escalation.
• Junior Mentorship & Documentation: Provide code review guidance to team; document model design decisions and evaluation outcomes to production-handoff standard.

Qualifications

Requirements

• Education: Bachelor's or Master's degree in AI, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred.
• Experience: 1-3 years of hands-on ML engineering experience, or equivalent depth demonstrated through internships, academic research, or open-source contributions. Must show component-level technical ownership within an end-to-end ML pipeline (training through deployment) - not just execution under direction. Kaggle rankings, arXiv preprints, or significant open-source ML contributions are valued as evidence of depth.

Must have Skills

• Python: Strong proficiency - primary ML development language
• PyTorch: Proficient - Expert - independent model training and evaluation
• Computer Vision: Strong foundation in CNNs, plus hands-on depth in at least one of: U-Net/segmentation, ViT/transformer-based vision, or time-series anomaly detection. Candidates with depth across multiple areas (e.g. full inspection-scope coverage - segmentation, transformer vision, and anomaly detection together) will be considered for the higher end of the band.
• Surrogate Modeling: Implement and iterate surrogate pipelines under P110 architectural guidance
• Active Learning: Configure acquisition functions; uncertainty-guided experiment scheduling
• Data-to-Model Interface: Define feature specs; validate incoming datasets against model requirements; flag data quality issues before training
• Practical MLOps: MLflow, Docker, Git, basic CI/CD contribution
• Model Evaluation & Uncertainty Analysis: Ablation studies, confidence calibration, validation methodology
• Technical Documentation: Model design decisions and evaluation results to production-handoff standard

Good to have Skills

• PINNs implementation under technical guidance
• Bayesian methods - Bayesian neural networks, Gaussian processes, ensemble uncertainty, calibration
• Reinforcement learning basics - gym environments, policy gradient concepts
• AWS fundamentals - S3, EC2, SageMaker basics; entry-level cloud ML deployment
• RAG pipeline fundamentals