Principal Engineer - Machine Learning

Western Digital; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Western Digital is seeking an ML Engineer to own the ML systems for product development defects detection, material modeling with limited data, and active learning selection in an AI-driven environment. You will implement CNN/U‑Net/ViT models, build real-time anomaly detection, and manage data-to-model interfaces, MLflow tracking, and CI/CD for reliable deployments.

Collaboration with data engineers and researchers is essential.

Qualifications

• Bachelor's or Master's degree in AI, ML, CS, or related field.
• 1–3 years hands-on ML engineering experience with end-to-end pipeline ownership.
• Strong Python and PyTorch proficiency; CV basics in CNNs, U-Net, ViT, or anomaly detection.

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 implementation of surrogate modeling and active learning pipelines.
• Define feature specifications and validate datasets to ensure model training quality.
• Maintain MLflow tracking, containers, and basic CI/CD contributions.
• Provide code review guidance and document 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 degree in AI/ML/CS

Tools

MLflow

Docker

Git

Job description

Company Description

WD is building the infrastructure behind the AI-driven data economy.

As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.

We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.

This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.

We’re looking for peoplewho want to build, solve, and operate at that level.

Join us and let’s shape the future of data.

Job Description

About This Role — The Mission

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 escalation of 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 orMaster's degreein 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: Implementand iterate surrogate pipelines under P110 architectural guidance
• Active Learning:Configure acquisition functions; uncertainty‑guided experiment schedulingData-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
• Reinforcement learning basics — gym environments, policy gradient concepts

Additional Information

WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [e-mail] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

WD is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [e-mail] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.