Top AI & Machine Learning Jobs in Singapore

Analyze semiconductor manufacturing, inline, parametric, probe, sensor, and fabrication-tool data to identify yield detractors and reduce defects. Build data pipelines, automate analysis, apply statistical modeling and machine learning, support Design of Experiments, and create dashboards and reports using Dash, Plotly, or Angular. Collaborate with manufacturing, process, equipment, and integration engineers to optimize processes and communicate findings.

Analyze semiconductor manufacturing data to identify yield detractors, reduce process variation, and improve device yield. Build data pipelines and automated processes using data from SQL databases, sensors, and fabrication tools. Apply statistical modeling, machine learning, and DOE analysis to solve production issues and support root cause investigations. Create dashboards and reports with visualization tools, collaborate with process engineering teams, and responsibly use AI-enabled tools to improve manufacturing outcomes.

Designs, develops, deploys, and governs AI-powered products, intelligent workflows, and autonomous multi-agent systems for HR and People organizations. Builds integrations across Workday, ServiceNow, Microsoft platforms, APIs, and enterprise data sources. Develops reusable AI frameworks, orchestration, tool calling, retrieval, monitoring, and evaluation capabilities while ensuring security, privacy, compliance, and responsible AI standards. Partners with business and technology leaders to identify transformation opportunities and deliver scalable solutions across recruiting, onboarding, employee services, learning, talent management, and workforce planning.

Lead AI-enabled cost transformation across procurement and manufacturing, build and operationalize a portfolio of AI/Generative AI use cases (spend, supplier, contract analytics), implement measurable savings and productivity improvements, embed solutions into workflows, manage cross-functional stakeholders, and scale data science and automation initiatives for value capture.

Develop and validate an AI-driven root cause analysis tool for semiconductor Chemical Vapor Deposition incident data. The intern will prepare and classify equipment alarms, process-control records, and human-written incident logs; train machine learning or generative AI models; identify recurring failure patterns; conduct correlation studies; and prototype Microsoft Teams integration for incident validation. The role includes data visualization, model validation, root cause analysis, and collaboration with cross-functional engineering experts.

Leads global data science and analytics teams supporting semiconductor manufacturing. The role applies statistical modeling, machine learning, data engineering, and visualization to optimize NAND process capability, device yield, and operational performance. Responsibilities include building data pipelines, analyzing manufacturing and equipment data, designing experiments, developing automated tools, mentoring technical talent, and managing cross-functional analytics programs. Strong semiconductor manufacturing expertise, Python, SQL, cloud platforms, and advanced engineering analytics experience are required.

Leads the redesign of complex business processes into autonomous, agentic AI systems. Responsibilities include process decomposition, AI architecture, agent workflow development, prototyping, integration, security controls, human-in-the-loop design, testing, production delivery, value measurement, and mentoring. The role collaborates with business, architecture, security, platform engineering, and project management teams to deliver scalable generative AI solutions.

Build and support end-to-end AI applications for semiconductor manufacturing, including data pipelines, machine learning models, APIs, web interfaces, automation tools, and observability. Develop Python and JavaScript/TypeScript solutions, integrate LLMs and agentic AI, connect robotics and manufacturing systems, and deploy containerized applications using Kubernetes or OpenShift. Contribute to CI/CD, Responsible AI, model evaluation, monitoring, testing, documentation, and optimization.

Lead global analytics and data science teams supporting semiconductor manufacturing. Drive yield and process optimization through SQL, sensor, and fabrication-tool data; apply statistical modeling and machine learning to reduce defects; support DOE experiments; build automated dashboards; and manage cross-functional delivery portfolios. Mentor technical talent, improve data quality and platform performance, standardize modern data stacks, and partner with engineering and business leaders on prioritization, risks, and dependencies.

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.