Top Machine Learning Jobs in Singapore

Five-month intern project focused on NAND probe product engineering, test mode integration, Design for Test, silicon validation, and product characterization. The intern will analyze engineering data for yield and failure patterns, develop visualization and automation tools, and explore AI, machine learning, generative AI, and LLM-based solutions for engineering workflows. Deliverables include technical assessments, dashboards, prototypes, reports, and recommendations.

Develop and optimize semiconductor assembly and packaging processes, characterize process windows, improve yield and reliability, and lead root-cause and failure-analysis investigations. Support NPI, product qualification, technology transfers, process readiness, and high-volume manufacturing. Use statistical methods, manufacturing datasets, visualization, machine learning, and AI-enabled monitoring to identify trends and optimize processes. Provide technical leadership, mentor engineers, and coordinate cross-functional teams across process development, manufacturing, yield, reliability, integration, and equipment engineering.

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.

Develops semiconductor product reliability stress tests, qualification plans, and manufacturing test flows for High Bandwidth Memory products. Investigates qualification and field failures using electrical and physical failure analysis, drives root-cause resolution, supports process conversions and high-volume manufacturing, and manages reliability risks and cross-functional projects. Applies Python, JMP, machine learning, and AI tools to analyze reliability data, identify anomalies, improve workflows, and reduce defect-per-million rates.

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.

Leads global strategy, governance, deployment, and continuous improvement of semiconductor front-end quality and equipment-control systems. Oversees FDC, alarms, equipment monitoring, risk assessment, lifecycle controls, analytics, automation, machine learning, and generative AI initiatives. Defines processes, requirements, standards, metrics, and best practices while partnering with fabs, IT, engineering, and analytics teams. Leads technical programs, prototypes, deployment, validation, cross-site alignment, and operational improvements supporting product quality, yield, reliability, and manufacturing continuity.

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.

Leads analytics for Wise’s financial crime and risk controls teams, turning complex data into actionable insights, metrics, reports, visualizations, and product recommendations. Partners with product managers, designers, data scientists, engineers, and operations teams to detect and mitigate financial crime while balancing risk with customer experience. Builds scalable risk frameworks, identifies emerging threats, and manages a team of three analysts.

The role involves fault isolation and failure analysis support, operating equipment for electrical defect localization, collaborating with teams for defect resolution, and leading a team of technicians.

Leads Micron’s global Photo MAP organization, overseeing photolithography, metrology, application, and process performance across semiconductor manufacturing sites. Develops technology roadmaps, improves productivity and overlay control, drives AI-enabled digital transformation, supports factory expansions, and standardizes best practices. Partners with manufacturing, technology development, process integration, and suppliers while building global teams, developing talent, managing strategic initiatives, and aligning manufacturing capabilities with business objectives.

The role involves electrical failure analysis, fault isolation, collaboration with product engineering, and process improvement strategies.

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.

As a CE PYE FA Engineer, you will perform fault isolation and failure analysis, collaborate with teams, and develop new techniques to enhance operational efficiency.

Design and deploy software and data-analytics solutions for Probe operations: real-time reporting, monitoring, automation of material tracking and validation, and AI-assisted efficiency improvements. Collaborate globally to identify process improvements and implement software and process changes to reduce resolution turnaround and ensure product quality.

This role involves leading NAND technology integration, solving technology gaps, designing experiments, and driving technical innovation in memory solutions.

Develop analytics, operations-research and machine-learning solutions and web applications to improve semiconductor manufacturing efficiency and scheduling. Manage requirements, stakeholders, and delivery; collaborate across teams; integrate AI-assisted tools; and support continuous improvement in capacity, cycle-time, and scheduling optimization.

Design, develop, deploy, and maintain automation software for factory operations including autonomous material delivery, scheduling, and equipment automation. Collaborate with manufacturing and global automation teams, own end-to-end solution lifecycle, troubleshoot production systems, use data analysis and visualization, and integrate AI-assisted tools to drive continuous improvement and operational efficiency.

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

Own the strategic roadmap for AI-enabled yield smart manufacturing solutions. Identify solution gaps, define business opportunities, and lead global projects with fab engineers, AI engineers, and data scientists. Apply AI, GenAI, predictive analytics, and machine learning to yield improvement, process optimization, technology development, and intelligent decision support. Use performance metrics and domain expertise to address manufacturing challenges and deliver integrated solutions.

Own smart manufacturing and AI-enabled yield analysis solutions from discovery through implementation. Diagnose process gaps, define requirements with Industry 4.0 users, standardize scalable tools, build ROI-based business cases, prioritize initiatives, and coordinate global teams including architects, data scientists, data engineers, and machine learning engineers. Use operational metrics to identify improvements and provide functional guidance on system capabilities and interfaces.

Owns pre-silicon validation, simulation regression, post-silicon characterization, and simulation-to-silicon correlation for NAND test chip vehicles. Analyzes device and architecture sensitivities, defines test methodologies, leads root-cause investigations, and drives silicon learning across design, process, technology, and test teams. The role also develops reusable technical methods, influences cross-functional decisions, mentors engineers, and applies data analytics, machine learning, and AI-assisted tools.

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.

Design and implement advanced process control, automation, and analytics solutions for semiconductor manufacturing. Collaborate with process teams, apply statistical analysis and machine learning to improve yields, maintain global control applications, and deliver training while promoting continuous improvement and responsible use of AI tools.

The role involves developing advanced thin film deposition processes, conducting experiments for process improvement, collaborating with teams, and mentoring junior engineers.

Lead a full-stack engineering team building scalable websites, CMS platforms, customer-facing applications, and growth infrastructure. Set technical direction across frontend, backend, APIs, content platforms, and integrations while enabling experimentation, personalization, SEO, performance, and conversion optimization. Mentor engineers, drive engineering excellence, and partner with Product, Design, Marketing, and Data Science to deliver measurable customer acquisition and growth outcomes.