Top Machine Learning Jobs in Singapore

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.

Conduct a five-month engineering project optimizing Chemical Mechanical Planarization processes and characterizing equipment in semiconductor manufacturing. Analyze process, metrology, equipment, and manufacturing data using statistical and AI-assisted methods; assess process capability and qualification results; and develop a dashboard, analytics model, or automation tool. Collaborate with cross-functional stakeholders and deliver technical reports, presentations, engineering recommendations, and documented improvement results focused on stability, yield, cost, productivity, or data accessibility.

Leads engineering, maintenance, commissioning, optimization, capacity planning, and expansion of high-, medium-, and low-voltage electrical infrastructure supporting semiconductor manufacturing. Oversees reliability, safety, risk assessments, incident investigations, energy efficiency, and construction design reviews. Mentors engineers and technicians while driving SMART power systems, predictive diagnostics, automation, AI-enabled tools, and data-driven continuous improvement.

Develop an AI-enabled application, analytical model, or automated workflow addressing a semiconductor process or equipment engineering challenge. Analyze manufacturing and engineering data using statistics, machine learning, Generative AI, large language models, visualization, or automation. Test, validate, document, and present the solution, demonstrating potential improvements in yield, quality, cost, productivity, or cycle time.

Own smart manufacturing product solutions from problem definition through deployment, adoption, and value realization. Gather business requirements, define solution scope and KPIs, prioritize improvement opportunities, and lead cross-functional projects with data scientists, data engineers, suppliers, and manufacturing stakeholders. Use operational data, AI, machine learning, and automation to improve wafer output, productivity, decision-making, and capital efficiency.

Leads High Bandwidth Memory product development from introduction through high-volume production. Owns technical program requirements, milestones, reviews, risk mitigation, decision-making, and cross-functional coordination across engineering, quality, supply chain, packaging, test, and manufacturing teams. Represents programs to executives, drives change management, and develops distributed team members through mentorship, coaching, evaluations, and accountability.

Analyzes logistics performance, costs, inventory flows, and service levels; develops dashboards and reports; leads global logistics transformation projects; implements AI, automation, and workflow improvements; manages SAP and logistics systems; coordinates testing, upgrades, training, governance, documentation, and continuous improvement initiatives.

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.

Analyze semiconductor manufacturing and measurement data to identify cumulative error variation in staircase and chop dry etch processes. Apply statistical analysis, data visualization, and AI-enabled workflows to determine variation sources, develop process control strategies, and recommend improvements to process margins, inline stability, yield, efficiency, and equipment productivity. Collaborate with engineering teams and present findings to department leadership.

The Staff/Senior Engineer will focus on fault isolation and failure analysis for electrical devices, collaborating with teams to resolve defect issues and improve processes.

Owns smart manufacturing and AI product initiatives focused on yield analysis and Industry 4.0 adoption. Responsibilities include diagnosing process gaps, defining requirements, standardizing scalable solutions, developing business cases and ROI analyses, prioritizing projects, coordinating global technical teams, and guiding system capabilities. The role partners with solution architects, data scientists, data engineers, and machine learning engineers while using performance metrics and AI-enabled tools to drive continuous improvement.

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.

Lead data science strategy for AML and financial crime risk controls. Develop AI-powered screening, entity resolution, graph and network intelligence, anomaly detection, transaction monitoring, and proactive discovery of emerging threats. Partner with Product, Engineering, Compliance, and Operations to deliver scalable production controls, influence the roadmap, establish reusable frameworks, and mentor data scientists.

Develop an AI-enabled quality engineering solution for semiconductor manufacturing workflows involving contamination control, incoming quality control, or chemical laboratory analysis. Analyze processes, apply statistical and machine learning techniques, build dashboards or automated workflows, evaluate business and technical impact, document findings, and present recommendations to engineering leaders.

Develop and evaluate a Python-based automated framework for Non-Zero-Offset control in high-volume semiconductor photo processes. The intern will analyze inline, recipe, metrology, and historical process data; apply statistical and machine-learning methods; create visualizations and automation prototypes; and validate findings with engineering subject matter experts. Deliverables include cleaned datasets, reusable scripts, predictive or risk-identification models, technical documentation, and a final presentation.

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.

Summer analysts will build and deploy machine learning, generative AI, and agent-based solutions; integrate complex datasets; develop predictive models; run experiments; analyze trends; and deliver actionable insights. They will also support data governance, risk management, data quality, privacy, and regulatory controls while collaborating with agile, cross-functional teams. The program includes mentorship, training, and potential full-time opportunities after graduation.

Develop a prototype AI-enabled equipment health monitoring and reliability analytics framework. Analyze alarms, downtime, maintenance, and performance data; create health indicators; apply statistical and machine learning methods for anomaly detection and degradation analysis; build an intelligent monitoring agent and interactive dashboard; and document and present findings to engineering stakeholders.

Participate in a five-month project optimizing NAND component test flows through manufacturing data analytics, automation, statistical analysis, machine learning, and enterprise AI. Retrieve, prepare, analyze, and visualize manufacturing data; develop scripts, models, reports, and automation tools; identify product recovery and test-flow improvement opportunities; validate AI-generated outputs; and present documented findings and recommendations. Collaborate with engineers while strengthening technical documentation, communication, and problem-solving skills.

Define and develop reliability stress tests and qualification plans for HBM products; drive DPM reduction, root-cause analysis (EFA/PFA), process conversions, risk management, cross-functional collaboration, and apply AI/ML tools to reliability datasets to improve test strategy and production yield.