Top Machine Learning Jobs in Singapore

Supports a five-month product engineering project involving NAND Flash, memory products, and SSDs. Analyzes test coverage, yield data, electrical measurements, and failure mechanisms; conducts circuit simulations and engineering evaluations; and develops scripts, dashboards, automation, or AI-enabled workflows. The intern documents findings, recommends improvements to product quality and test efficiency, and presents a final technical report under experienced engineering guidance.

Develop an end-to-end analytics and machine learning solution for semiconductor manufacturing data. Analyze electrical, physical, inline, and probe measurements; perform data querying, preprocessing, feature engineering, modeling, optimization, and visualization; and create predictive models and drift-detection methods to improve production-line monitoring and engineering decisions. The intern will document and present findings while collaborating with manufacturing engineering teams.

Supports advanced copper sputter process development for HBM semiconductor metallization. Responsibilities include process characterization, hardware qualification, integration assessment, statistical and machine-learning analysis, defect reduction, yield improvement, and manufacturing readiness. The intern will use AI, manufacturing systems, and data analytics to identify process trends and recommendations while collaborating with process integration, manufacturing, equipment, and quality teams.

Conduct silicon design validation for advanced NAND products through Verilog simulations, validation planning, laboratory measurements, and data analysis. The intern will use testers, oscilloscopes, probe stations, scripting, and engineering software to analyze validation data, generate technical reports, and prototype AI-enabled workflows for automated data collection, reporting, or anomaly detection.

Full-time internship focused on applying Python, statistical analysis, visualization, and introductory machine learning to semiconductor probe-test, electrical failure, and fabrication-process data. The intern will investigate yield behavior in advanced memory manufacturing, develop a multivariate analytics tool, identify potential yield-improvement opportunities, and present documented findings through a technical report and presentation.

Develop an AI-enabled proof of concept for New Product Manufacturing, using Generative AI, analytics, dashboards, automation, Python, SQL, and Microsoft tools. The intern will assess manufacturing processes, define data and business requirements, automate reporting or workflows, create operational visualizations, measure productivity benefits, and document recommendations. The role involves collaboration with engineering, manufacturing, data, automation, and business stakeholders.

Intern will analyze Wafer Intelligent Scanner, defect, yield, process, and equipment data to identify abnormal wafer bevel behavior and early indicators of defects. Responsibilities include data preparation, statistical and machine learning analysis, model evaluation, dashboard creation, and technical reporting. The project supports semiconductor yield improvement and defect reduction through AI-enabled manufacturing analytics.

Contribute to semiconductor manufacturing EHS projects involving compliance tracking, risk monitoring, environmental compliance, safety initiatives, data analysis, dashboards, automation workflows, and AI-enabled solutions. The intern will complete an improvement project, develop recommendations and an implementation plan, and present findings. The five-month internship provides practical experience with ISO 14001, ISO 45001, workplace safety, risk assessment, digitalization, and continuous improvement.

Develop an IoT-based monitoring and anomaly-detection solution for photolithography equipment in a semiconductor manufacturing environment. The intern will study equipment behavior and sensor signals, build prototypes for data ingestion, processing, analytics, and visualization, and apply Python, machine learning, and signal-processing techniques. Responsibilities include integrating equipment or process data, analyzing imaging, acoustic, environmental, motor, or material-usage data, documenting results, and presenting a final technical report.

Intern will develop software, data pipelines, integrations, dashboards, automation, and AI-enabled solutions for photolithography engineering in semiconductor manufacturing. Responsibilities include connecting equipment, manufacturing systems, databases, and engineering platforms; applying analytics and machine learning; and using modern software practices such as version control, testing, deployment, and maintainable architecture. The intern will collaborate with multidisciplinary engineering teams and deliver production-quality applications, documentation, reusable code, and a final technical presentation.

Drive dry etch process and equipment performance using advanced data analytics, automation, machine learning, statistical modeling, and generative AI. Lead cross-functional initiatives integrating manufacturing, equipment, process, and business data. Develop scalable analytics solutions to improve productivity, tool utilization, cycle time, excursion response, process control, and manufacturing costs. Collaborate with suppliers and internal digital teams to evaluate and deploy AI technologies for semiconductor manufacturing.

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.

Intern will develop AI-enabled analytics, automation workflows, dashboards, and software solutions for NAND semiconductor test engineering. Responsibilities include analyzing manufacturing and test datasets, automating test-result analysis and validation, correlating wafer yields, exploring machine learning for predictive insights, and improving test quality, productivity, and engineering cycle time. The role provides hands-on exposure to wafer probe, burn-in, assembly testing, and high-volume semiconductor manufacturing while collaborating with engineering, manufacturing, quality, and data science teams.

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.

Manages end-to-end procurement for capital equipment, spares, and services, including P2P processes, supplier relationships, negotiations, contracts, delivery, cost optimization, and performance management. Drives localization, supply-chain resilience, continuous improvement, and AI-enabled procurement initiatives. Partners with Procurement, Engineering, Planning, Finance, and Legal teams while providing strategic recommendations and executive-level supplier insights.

Owns development, qualification, and deployment of advanced semiconductor metrology applications for 3D NAND process development. The role focuses on FIB and complementary techniques such as AFM, SEM, and WLI, while evaluating next-generation systems, developing technology roadmaps, partnering with suppliers, and applying AI, machine learning, and data analytics to improve defect detection, yield, reliability, and manufacturing readiness.

Intern will map High Bandwidth Memory sample workflows, clean and analyze manufacturing data, develop predictive models, forecast burn-in resource demand, and build Power BI or Tableau dashboards and automated alerts. The role supports workflow optimization, risk monitoring, process documentation, and stakeholder recommendations across semiconductor planning and reliability operations. Candidates gain experience in predictive analytics, business intelligence, AI-enabled workflows, and manufacturing systems.

Interns will develop AI-enabled and digital solutions for semiconductor failure analysis workflows. Responsibilities include analyzing laboratory metrics and engineering datasets, creating dashboards and automation tools, exploring AI and knowledge-management applications, collaborating with Test Engineering stakeholders, and presenting project findings. The role provides exposure to electrical and physical failure analysis, semiconductor manufacturing, data analytics, workflow automation, and engineering decision-making.

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.

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.

Intern will develop and deploy a digital Target Traveler solution for semiconductor Probe operations. Responsibilities include analyzing process flows, defining control requirements, configuring workflows, creating data collection and compliance dashboards, monitoring deviations, and recommending process improvements. The role involves collaboration with engineering, manufacturing, quality, and IT teams while building experience in semiconductor process control, analytics, workflow automation, digital transformation, and AI-enabled manufacturing.

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.

Develops and optimizes semiconductor manufacturing process control systems to improve yield, quality, reliability, productivity, and cost. Responsibilities include data analysis, modeling, SPC, APC, FDC, process and equipment evaluation, baseline qualification, supplier coordination, control solution implementation, training, and network-wide Industry 4.0 deployment. The role uses machine learning, IIoT, deep learning, and big data platforms to identify process variation and implement smart manufacturing solutions.

Lead APAC payments transformation initiatives from strategy through execution. Responsibilities include identifying growth and efficiency opportunities, conducting quantitative and root-cause analyses, developing business cases, managing project governance, optimizing processes and operating models, assessing regulatory risks, creating mitigation strategies, and coordinating cross-functional stakeholders to deliver scalable solutions.

Build and own test automation, frameworks, test data, isolated environments, contract tests, and quality gates for risk decisioning systems. Integrate API, end-to-end, regression, smoke, and post-release verification testing into CI/CD. Validate jurisdiction-specific behavior, mentor engineers, support release acceptance, and develop testing approaches for ML models and LLM agents.