Singapore, Singapore
Live, first seen by us 38 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Design and own CNN, U-Net, and ViT systems for inspection and defect classification
Own real-time anomaly detection on sensor and time-series product data
Validate and deploy physics-informed AI (PINNs) into product development systems
Architect active learning pipelines that reduce physical experiment cost
Architect and own the full ML platform, including MLflow, Docker, and AWS EKS
Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification
Own real-time anomaly detection from sensor and time-series product development data streams
Validate and deploy PINNs methodology originated by the ARS
Architect active learning pipelines and Bayesian experimental design frameworks
Own the full ML platform: MLflow, Docker, AWS EKS/Kubernetes
Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred. Equivalent depth demonstrated through open-source contributions, or significant GitHub portfolio will be considered.
Experience in materials science, semiconductor, precision product development, or equivalent scientific/industrial AI is a strong differentiator.
Python: Expert-level proficiency - Primary language for all ML System work
Education - Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred. Equivalent depth demonstrated through open-source contributions, or significant GitHub portfolio will be considered. Experience
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Day 38
Similar jobs usually come down between day 8 and day 76.
Well past Western Digital’s usual: its jobs come down by about day 14.
From 5,465 similar postings that came down, and 60 of Western Digital’s. Coming down is not always a hire.
14 open jobs
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Must have Skills: - Python: Expert-level proficiency - Primary language for all ML System work - PyTorch: Expert - Custom loss functions, full training loop ownership
- Active Learning Pipeline Architecture: Architect active learning pipelines and Bayesian experimental design frameworks — integrating ARS-designed acquisition functions with DE-supplied feature data and laboratory scheduling systems. This workstream directly reduces physical experiment cost. - MLOps Platform Ownership: Own the full ML platform: MLflow, Docker, AWS EKS/Kubernetes, LLM Gateway (LangFuse/PortKey), CI/CD, observability, and model monitoring. Architect the platform; delegate maintenance to team once stable. - Technical Direction & Team Architecture: Lead design reviews and code reviews; provide technical direction to team; define data interface contracts with the DE; partner with domain scientists from problem definition through deployment; mentor junior team members.
- Active Learning & Bayesian Experimental Design: Acquisition function pipelines; laboratory scheduling and instrument control integration - Product Development MLOps: MLflow, Docker, AWS EKS, Observability Platforms like LangFuse, PortKey or other LLM Gateways) Good-to-Have Skills:
- RLHF & reward modeling — domain expert feedback integration - AWS · LLM Finetuning (LoRA, QLoRA) · LLM Distillation Additional Information
- Python: Expert-level proficiency - Primary language for all ML System work - PyTorch: Expert - Custom loss functions, full training loop ownership - Computer Vision: CNN, U-Net, ViT — full architecture range for inspection, defect detection, and measurement
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
About This Role — The Mission
Precision product development is one of the last frontiers where AI has not yet been systematically deployed at the physics level. We are changing that. As the ML Technical Lead on this team, you will be the person who makes physics-informed AI work in product development — not as a research prototype, but as a deployed system that drives real engineering decisions. If that is the kind of problem you want to work on, we want to talk to you.
Key Responsibilities
Requirements
Education
Experience
Must have Skills:
Good-to-Have Skills:
#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 jobs.accommodations@wdc.com 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 compliance@wdc.com.
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.
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Singapore, Singapore
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