Senior Technologist - Machine Learning

About the role

Western Digital is looking for a Senior Technologist (Machine Learning) in Singapore to act as the ML Technical Lead on a team focused on precision product development. The role turns physics-informed AI into deployed systems that drive real engineering decisions, rather than research prototypes. The position is full-time.

Key responsibilities

• Design and own CNN, U-Net and ViT systems for precision inspection, measurement and defect classification, and real-time anomaly detection from sensor and time-series product data.
• Validate and deploy physics-informed neural networks (PINNs) and surrogate model pipelines, validating physics constraints against engineering requirements.
• Architect active learning pipelines and Bayesian experimental design frameworks that integrate acquisition functions with feature data and laboratory scheduling systems.
• Own the full ML platform (MLflow, Docker, AWS EKS/Kubernetes, LLM gateway, CI/CD, observability and model monitoring), then delegate maintenance once stable.
• Lead design and code reviews, define data interface contracts with the domain engineering team, partner with domain scientists and mentor junior team members.

Required profile

• Bachelor's or Master's degree in AI, machine learning, computer science or a related field; AI major or strong research focus preferred.
• Minimum 3-5 years of hands-on technical experience owning AI systems from design to product deployment.
• Proven direction of a small ML or AI engineering team, with evidence of design reviews, mentoring and cross-functional delivery.
• Track record of shipping ML models into product development; experience in materials science, semiconductors or precision product development is a strong differentiator.

Required skills

• Expert Python and PyTorch, including custom loss functions and full training loops.
• Computer vision (CNN, U-Net, ViT), real-time anomaly detection, surrogate modeling, active learning and Bayesian experimental design.
• Product development MLOps: MLflow, Docker, AWS EKS, observability platforms such as LangFuse or PortKey.
• Good to have: PINNs, reinforcement learning, RAG / GraphRAG, RLHF, LoRA/QLoRA and LLM distillation.