Top AI & Machine Learning Jobs in Singapore

Develop AI-enabled automation workflows for NAND device characterization and engineering data analysis. Build data pipelines, machine learning models, anomaly detection tools, dashboards, AI assistants, and automated reporting solutions using large-scale semiconductor datasets. The role involves applying data science, generative AI, LLMs, and agentic AI to improve engineering productivity and decision-making while collaborating with cross-functional engineering teams.

Build and deploy full-stack AI applications for smart manufacturing, including data pipelines, machine learning models, APIs, web interfaces, LLM integrations, automation tools, and observability. Develop containerized solutions with Docker and Kubernetes/OpenShift, contribute to CI/CD and MLOps practices, integrate robotics and manufacturing systems, and apply Responsible AI controls. Collaborate with data scientists, software engineers, and manufacturing stakeholders to improve operational decision-making and autonomous operations.

Analyze semiconductor manufacturing data to identify yield detractors, optimize processes, and reduce defects. Build data pipelines from SQL databases, sensor networks, and fabrication logs; apply statistical modeling, machine learning, SPC, DOE, and related analytics. Create automated dashboards and reports using Dash, Plotly, or Angular, and collaborate with manufacturing, equipment, process, and integration engineers to resolve production issues and drive continuous improvement.

Design, build, and deploy production AI applications for semiconductor manufacturing. Responsibilities include developing full-stack machine learning solutions, APIs, web applications, data pipelines, LLM and agentic AI capabilities, analytics, and integrations with robotics, automation, control systems, and MES platforms. The role also leads deployment, observability, CI/CD, model evaluation, responsible AI, optimization, testing, mentoring, and technical documentation across global manufacturing environments.

Leads the architecture and delivery of production AI products for autonomous semiconductor manufacturing. Responsibilities span data engineering, machine learning, GenAI, full-stack applications, robotics integration, digital twins, simulation, and edge-to-cloud deployment. The role also owns CI/CD, observability, model evaluation, Responsible AI, performance optimization, technical mentorship, and cross-functional delivery across software, manufacturing, robotics, and infrastructure teams.

Develop an AI agent for NAND product-development project execution, including project-risk detection, mitigation recommendations, workflow integration, and evaluation. The intern will study engineering workflows, prototype AI applications using generative AI, large language models, agentic frameworks, and data analytics, then deploy and document the solution while collaborating with product-development engineers and stakeholders.

Develop machine learning models and agentic AI prototypes for semiconductor yield, reliability, testing, and manufacturing analytics. Prepare and transform high-volume engineering data, build feature-engineering pipelines, evaluate models, and explore workflow automation using engineering and enterprise tools. Deliver technical documentation, demonstrations, and presentations while collaborating with Product Engineers and AI mentors.

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.

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.

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.