Senior ML Systems Engineer: End-to-End Model Ownership

WD Media (Singapore) Pte Ltd; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

WD Media (Singapore) Pte Ltd seeks a skilled ML Engineer to own end-to-end ML systems for product development defects detection and data-driven experiments. You will deploy CNN/U‑Net/ViT models and manage anomaly detection pipelines in a production environment.

The role requires hands-on ML engineering with Python, PyTorch, and experience in surrogate modeling, active learning, and MLOps practices. Collaboration and documentation are essential for production handoff.

Qualifications

• Bachelor's or Master’s degree in AI, Machine Learning, Computer Science, or related field.
• AI major or strong AI research focus preferred.

Responsibilities

• Deep Learning Model Implementation & Product Development Ownership: Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement.
• Anomaly Detection Systems: Build and maintain real-time anomaly detection for product development sensor and time-series data streams.
• Surrogate Modeling & Active Learning Operations: Own implementation and iteration of surrogate model pipelines and active learning systems.
• Data-to-Model Interface Ownership: Define feature specs, validate datasets against model input requirements, and elevate data quality issues before training.
• MLOps Maintenance & Product Development Reliability: Maintain model versions, training pipelines, and containers under platform architecture.
• Junior Mentorship & Documentation: 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/Master's in AI/ML/CS

Tools

MLflow

Docker

Git

Job description

WD Media (Singapore) Pte Ltd seeks a skilled ML Engineer to own end-to-end ML systems for product development defects detection and data-driven experiments. You will deploy CNN/U‑Net/ViT models and manage anomaly detection pipelines in a production environment.

The role requires hands-on ML engineering with Python, PyTorch, and experience in surrogate modeling, active learning, and MLOps practices. Collaboration and documentation are essential for production handoff.