Western Digital

Staff Engineer - Machine Learning

Posted Yesterday

Be an Early Applicant

Singapore, SGP

In-Office

Entry level

Big Data • Cloud • Hardware • Software

The Role

Build and evaluate machine learning models for precision product development, including CNN-based image classification and defect detection. Validate training data, track experiments with MLflow, produce evaluation reports, package models with Docker, contribute to CI/CD, and support inference and deployment validation. The role also provides exposure to physics-informed AI, Bayesian methods, surrogate modeling, active learning, and scientific ML systems.

Summary Generated by Built In

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 people who 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

You will be one of the engineers on a focused AI team solving hard scientific problems in precision product development. You will own two real responsibilities from day one — not toy tasks, but actual team workflow contributions that senior engineers depend on. You will be introduced to physics-informed AI, Bayesian methods, and product development ML systems within your first year under direct mentorship from engineers and researchers who have worked at world-class institutions. If you are the kind of person who learns fast and wants to be in the middle of hard problems early in your career, this is an unusual opportunity.

Key Responsibilities

Experiment Tracking & Evaluation Reporting: Own MLflow experiment logging for assigned team model runs; conduct model evaluations using standard metrics; produce structured evaluation reports reviewed by team. Your reports directly inform model iteration decisions.
Training Data Quality Validation: Validate training datasets jointly with team — feature distribution checks, label verification, anomaly flagging. Your quality flags are the final check before data enters the model training pipeline. You close the data quality loop between workstreams.
Deep Learning Model Contribution: Build and train CNN-based models for image classification and defect detection under team’s guidance. Contribute to model evaluation cycles, configuration comparisons, and training run analysis.
ML Pipeline Contribution: Package models in Docker; contribute to CI/CD scripts under guidance; run inference tests and support deployment validation in product development environments.

Qualifications

Requirements

Education:

Bachelor's or Master's degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or related field. AI major or strong research focus in degree strongly preferred.

Experience:

Fresh graduate to 1 year. Work experience not required — demonstrated ML project competency is the primary criterion. Strong final-year project or thesis with a clear ML component; research internship preferred.

Must Have Skill:

Python: Strong proficiency — clean, readable ML code; NumPy/Pandas basics
PyTorch: Foundational — build, train, and evaluate a basic neural network independently from scratch
CNN Architecture Basics: Understand and implement a basic image classifier; conceptual understanding of convolutional layers
Surrogate Modeling Concepts: Why data-efficient ML matters in limited-data scientific settings
Active Learning Awareness: Conceptual understanding of uncertainty-guided data selection
MLflow Basics: Log experiments, parameters, and metrics for a training run
Docker Basics: Write a Dockerfile to containerize a Python/ML application
Model Evaluation: Standard metrics; produce a structured evaluation report
Learning Mindset: Self-directed learning outside coursework; evidence of picking up new concepts quickly.

Good-to-Have Skill:

U-Net or ViT exposure — academic project or course sufficient
Uncertainty quantification basics — Monte Carlo dropout, ensemble methods
Bayesian methods introduction — any probabilistic ML course or project
Time-series or sensor data — any project with sequential or temporal data
RL introduction — any RL course or gym environment experiment
RAG basics, any LLM project
AWS fundamentals; entry-level cloud ML deployment

Additional Information

#LI-FN1

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 [email protected] 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.

Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [email protected].

Notice To Candidates: Please be aware that WD and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to WD Ethics Helpline or email [email protected].

Skills Required

Bachelor’s or Master’s degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field
Strong proficiency in Python, including clean ML code and NumPy/Pandas basics
Foundational PyTorch skills, including independently building, training, and evaluating a basic neural network
Understanding and implementation of basic CNN image classifiers and convolutional layers
Understanding of surrogate modeling concepts and data-efficient machine learning
Conceptual understanding of active learning and uncertainty-guided data selection
Basic MLflow skills for logging experiments, parameters, and metrics
Basic Docker skills, including writing a Dockerfile for a Python or ML application
Ability to use standard model evaluation metrics and produce structured evaluation reports
Self-directed learning ability and evidence of quickly learning new concepts
U-Net or Vision Transformer exposure
Basic uncertainty quantification, such as Monte Carlo dropout or ensemble methods
Introductory Bayesian methods knowledge
Time-series or sensor data project experience
Introductory reinforcement learning experience
Retrieval-Augmented Generation or other LLM project experience
AWS fundamentals and entry-level cloud ML deployment experience
Strong final-year ML project or thesis; research internship experience

Western Digital Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Western Digital and has not been reviewed or approved by Western Digital.

Strong & Reliable Incentives — Strong & Reliable Incentives: Incentive structures in variable‑pay roles are portrayed as well‑designed, and annual or quarterly bonuses are commonly part of total compensation.
Healthcare Strength — Healthcare Strength: Company materials highlight comprehensive medical, dental, vision, and mental‑health resources, complemented by options like HSA/FSA and disability coverage.
Parental & Family Support — Parental & Family Support: Caregiving support across life stages and children’s behavioral health resources are featured, with programs such as Bright Horizons referenced for U.S. employees.