Top AI & Machine Learning Jobs in Singapore

Develop end-to-end data science and digital twin solutions for manufacturing, planning, and supply chain operations. Responsibilities include business problem framing, data analysis, mathematical optimization, discrete event simulation, software and pipeline development, API and UI creation, deployment, user adoption, benchmarking, and continuous improvement across global manufacturing sites.

Own the product backlog and delivery of AI and agentic solutions for procurement and planning. Translate stakeholder needs into requirements, user stories, data requirements, and implementation plans. Collaborate with data scientists and engineers to assess data readiness, develop solutions, and drive products from discovery through deployment, adoption, and value realization. Identify opportunities to improve cost efficiency, productivity, and capital efficiency through AI, automation, and workflow optimization.

Develop an AI-enabled optimization engine for wafer test movement configurations, using machine learning and optimization techniques. Analyze historical semiconductor data, benchmark recommendations, build a web application for visualization and comparison, and validate improvements in test time and tester utilization. The role involves collaboration with Automation, Equipment, and Engineering teams and requires a full-time commitment of at least five months.

Develop and deploy predictive models and AI/agentic AI solutions using large-scale manufacturing data. Perform EDA, data extraction/cleansing, feature engineering, and statistical modeling. Collaborate with data engineers, manufacturing engineers, UX, and stakeholders to translate insights into operational improvements that boost yield, quality, efficiency, and business value.

Analyze semiconductor manufacturing and equipment datasets to identify Germhold patterns and root causes. Apply statistical analysis, machine learning, predictive analytics, and data visualization to develop improvement insights, dashboards, and reporting tools. Collaborate with process and equipment engineers to validate findings, evaluate AI-enabled solutions, and recommend ways to improve workstation efficiency, reduce Germholds, and enhance manufacturing capacity.

Five-month internship focused on applying data analytics and machine learning to semiconductor manufacturing data. The intern will query and preprocess large-scale quality datasets, develop models for quality-metric drift detection, perform multivariable analysis and optimization, create visualizations, and automate engineering workflows. Responsibilities include collaborating with engineering teams, documenting results, and presenting recommendations. Relevant backgrounds include engineering, statistics, mathematics, computer science, data science, or another analytics-related discipline.

Develop and deploy machine learning, statistical modeling, and predictive analytics solutions for semiconductor manufacturing and business operations. Analyze large datasets, build scalable data workflows, conduct exploratory and root-cause analysis, and partner with engineering and software teams to operationalize models. Lead analytics projects, communicate insights to technical and business stakeholders, mentor junior data scientists, and ensure responsible, secure, compliant use of AI and data.

Build and productionize machine learning models for semiconductor quality and manufacturing processes. Develop agentic AI, LLM, retrieval-augmented generation, tool-use, and memory capabilities for predictive quality workflows. Create maintainable analytical pipelines, optimize performance and token efficiency, and support testing, deployment, and monitoring. Communicate analytical findings to technical and non-technical stakeholders while applying AI tools responsibly.

Architect and deploy autonomous Agentic AI systems for smart manufacturing. Responsibilities include developing multi-step AI agents, integrating LLMs with APIs and databases, building evaluation and profiling frameworks, implementing MCP and A2A interoperability, deploying agents through CI/CD to Kubernetes and cloud containers, securing agent workflows, optimizing inference performance, and mentoring engineers.

Supports the AI Process Intelligence team through Python automation prototypes, AI agent proofs of concept, predictive modeling, data pipelines, system connectors, controlled model testing, and technical documentation. Works under senior engineers and process architects in a structured, non-production environment while learning AI architecture, automation workflows, API integration, Agile development, DevOps, and knowledge integration.