Top Machine Learning Jobs in Singapore

This internship involves assisting in machine learning development, AI automation, data engineering, and exploring innovative projects to enhance test efficiency and engineering productivity.

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.

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.

Drives semiconductor diffusion process and equipment performance, productivity, capacity, yield, reliability, and cost improvements. Leads technical investigations, bottleneck reduction, supplier engagement, technology transfers, and cross-functional engineering projects. Uses data analytics, AI, machine learning, statistical methods, automation, and predictive maintenance approaches to improve manufacturing operations. Develops best practices, supports new capabilities, provides technical recommendations, and mentors junior engineers.

Leads global semiconductor manufacturing excellence initiatives, including SEAL deployment, best-known-method standardization, Wafer Per Day improvements, equipment and process optimization, and productivity programs. Identifies and scales AI-enabled engineering solutions, automation, advanced analytics, and digital workflows across manufacturing sites. Partners with global stakeholders, drives pilot adoption, tracks business impact, supports standardization, and mentors teams on AI utilization.

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.

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.

Develop an AI-enabled dashboard for CMP equipment health and wafer breakage risk prediction. Analyze semiconductor manufacturing and equipment datasets, correlate maintenance and process indicators with breakage and yield excursions, build risk-scoring models, and create Power BI visualizations and automated engineering reports. The intern will collaborate with equipment engineers and gain experience in predictive analytics, reliability engineering, statistical analysis, and semiconductor manufacturing.

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.

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.

Develop an AI-enabled automation solution for low-yield analysis in semiconductor memory testing. The intern will study test operations, manufacturing systems, test methodologies, data flows, and lot-disposition processes; apply programming, analytics, visualization, and AI tools; validate improvements in efficiency, accuracy, and consistency; and document findings through a final report and presentation.

Leads semiconductor manufacturing and equipment capability roadmaps, supplier technology partnerships, global manufacturing strategy, and cross-site productivity initiatives. Improves CVD equipment performance, reliability, throughput, utilization, cycle time, and cost efficiency through continuous improvement, root-cause analysis, technology transfer, and process qualification. Applies AI, machine learning, advanced analytics, and digital manufacturing solutions to optimize equipment and manufacturing operations while mentoring engineers.

Intern will analyze NAND wafer probe and device-level test data, evaluate yield trends and failure signatures, optimize test coverage and efficiency, and develop AI-enabled analytics or automation solutions. Responsibilities include statistical analysis, anomaly detection, data visualization, technical reviews, and collaboration with design, manufacturing, reliability, and test engineering teams. The intern will deliver technical recommendations, an analytical prototype or workflow, and a final report and presentation.

An off-cycle quantitative trading and research internship focused on derivatives pricing, risk management, electronic execution, systematic trading, and portfolio optimization. The intern will build mathematical and machine learning models, develop pricing and hedging tools, research trading strategies, conduct scenario analysis, assess model risk, and implement high-performance analytics in production systems while collaborating with traders, risk managers, and banking teams.

Three-to-six-month quantitative trading and research internship developing mathematical models, pricing and risk tools, algorithmic trading strategies, portfolio analytics, and high-performance software. Interns apply machine learning and advanced statistics to derivatives pricing, hedging, electronic execution, market making, risk management, and systematic trading. The role involves quantitative research, scenario analysis, model validation, production implementation, collaboration with traders and risk managers, and communicating technical findings.

Full-time intern developing an AI-enabled predictive analytics framework for SSD test equipment in a semiconductor manufacturing environment. Responsibilities include extracting, cleansing, and analyzing engineering data; building equipment health dashboards; performing statistical analysis; creating anomaly detection methods; identifying predictive maintenance opportunities; documenting results; and presenting recommendations. The role involves Python or SQL, data visualization, AI and machine learning concepts, equipment reliability, and collaboration with cross-functional engineering teams.

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 governance, compliance, continuous improvement, and digital transformation for global frontend capital management processes. Responsibilities include capital forecasting, funding and savings cycles, reporting, process standardization, dashboard development, analytics, automation, and cross-functional program leadership. Partners with planning, finance, procurement, facilities, and business stakeholders to improve data quality, operational efficiency, adoption, and decision-making.

Lead a distributed Process Integration team for HBM NPI, set PI strategy and governance, coordinate cross-site NPI execution, manage risks and yield ramp, drive AI-enabled analytics adoption, and build leadership and capability across sites.

Supports deployment and commissioning of real-time particle monitoring systems in semiconductor cleanrooms. Coordinates with vendors, facilities, quality, and manufacturing teams; assists with project planning, implementation troubleshooting, system verification, and performance monitoring. Applies manufacturing data analytics, trend analysis, anomaly detection, machine learning, and equipment health indicators. Documents project outcomes and recommends future optimization opportunities.

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 CE PYE FA Engineer will provide fault isolation and failure analysis support, collaborate on defect resolution, and innovate analysis techniques.

Lead HBM product reliability test development and execution to reduce DPM, drive intrinsic/extrinsic reliability, debug qual/RMA failures via EFA/PFA, enable process conversions with fab, apply ML/AI to reliability datasets, mentor team, and collaborate cross-functionally to meet quality, cost, and cycle-time KPIs.

Conduct experiments to improve poly-SiGe gap filling in high-aspect-ratio semiconductor structures. Analyze deposition effects on film growth, conformality, voids, seams, pinch-off, and structural integrity using DOE, SEM/TEM cross-sections, and statistical methods. Correlate fill quality with die strength and wafer or block bending, then recommend optimized process windows and qualification plans. Collaborate with engineering teams and subject matter experts throughout the development cycle.