ML Technical Lead - Physics-Informed AI for Precision

WD; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

WD seeks an ML Technical Lead to deploy physics-informed AI in precision inspection and product development. You will design CNN, U-Net, and ViT systems, implement real-time anomaly detection, and own the ML platform including MLflow, Docker, AWS EKS, and observability.

Lead a small AI engineering team and collaborate with problem-domain scientists through deployment. Ideal candidates have 3–5 years of hands-on AI experience, demonstrated ownership from design to production, and a strong track

Qualifications

• Bachelor's or Master in Artificial Intelligence, Machine Learning, Computer Science, or related field. AI major or strong AI research focus preferred.
• Minimum 3–5 years of hands-on technical experience.
• Demonstrated technical ownership of AI systems from design to product deployment.
• Proven direction of a small ML/AI engineering team with mentoring and cross-functional delivery.
• Track record of shipping ML models into product development.

Responsibilities

• 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 data streams.
• Validate and deploy PINNs, physics-informed AI for product development.
• Architect active learning pipelines and Bayesian experimental design frameworks.
• Own the full ML platform: MLflow, Docker, AWS EKS, observability, and model monitoring.

Skills

Python

PyTorch

Computer Vision

Anomaly Detection

Surrogate Modeling

Active Learning

MLOps

Docker

AWS EKS

LangFuse/PortKey

Team Leadership

Education

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

Tools

MLflow

Job description

WD seeks an ML Technical Lead to deploy physics-informed AI in precision inspection and product development. You will design CNN, U-Net, and ViT systems, implement real-time anomaly detection, and own the ML platform including MLflow, Docker, AWS EKS, and observability.

Lead a small AI engineering team and collaborate with problem-domain scientists through deployment. Ideal candidates have 3–5 years of hands-on AI experience, demonstrated ownership from design to production, and a strong track