Top AI & Machine Learning Jobs in Singapore

As a Data Science Engineer, you'll analyze big data, develop models, deploy solutions, and communicate insights to improve products and services.

The internship involves hands-on learning in data analytics and machine learning. Interns will build data-driven models, perform statistical analyses, and communicate insights to improve process integration outcomes in semiconductor technology.

Analyze semiconductor manufacturing data to identify yield detractors, build data pipelines, apply statistical and machine learning models for yield improvement, support DOE experiments, and create automated dashboards and reports for engineering stakeholders.

Design, build, deploy, and support end-to-end AI applications for manufacturing: data pipelines, feature engineering, model training/inference, web UIs/APIs, containerized deployments, LLM/RAG integrations, MLOps, observability, and Responsible AI practices.

Design and implement advanced scheduling, optimization, and simulation solutions in manufacturing to improve production metrics and drive innovation.

The Staff Data Scientist will optimize semiconductor manufacturing processes through data analysis, statistical modeling, and data visualization, collaborating closely with engineering teams.

The role involves developing AI-driven solutions for semiconductor engineering, focusing on GenAI systems, data pipelines, and machine learning models while collaborating with cross-functional teams.

Develop and deploy optimization, simulation, and ML models to improve semiconductor factory capacity, throughput, and cycle time. Partner with cross-functional manufacturing and engineering teams to analyze constraints, run scenario analyses, and deliver data-driven recommendations integrated into production pipelines and decision-support tools.

Lead design and deployment of agentic AI for manufacturing planning and operations. Build multi-agent systems combining LLM reasoning and optimization (MIP, heuristics, simulation), integrate agents with data pipelines and enterprise systems, and deliver solutions for capacity planning, production flow optimization, and decision automation. Partner with planning, operations, data engineering, and MLOps teams to translate domain requirements into deployable AI workflows and reusable orchestration frameworks.

Lead cross-functional projects to develop Smart Manufacturing solutions using AI/ML and BI to improve wafer output, run rate, equipment uptime, and process harmonization. Gather requirements, prepare data, collaborate with data scientists to build models, develop user-facing applications, integrate with systems, scale global solutions, and measure performance improvements.