Top Tech Jobs & Startup Jobs in Singapore

Intern will analyze semiconductor backend test processes and equipment performance, evaluate new technologies for high-volume manufacturing, identify yield, quality, reliability, cycle-time, and cost improvements, and apply structured problem-solving, risk assessment, FMEA, data analysis, and responsible AI methods. Deliverables include baseline analyses, performance trend findings, qualification summaries, recommendations, technical reports, presentations, and potentially an AI-enabled proof of concept.

Intern will assess manufacturing site conditions and strategic layouts, collaborate with Manufacturing, Engineering, Facilities, and Tool Owners, analyze planning data, and develop automation, dashboards, or data-management solutions using Python, Power Automate, SQL, and AI-enabled tools. The project focuses on improving layout feasibility assessment, reporting, information organization, and planning workflow efficiency.

Manage the lifecycle of software applications supporting automated semiconductor manufacturing. Analyze business and technical requirements, develop secure solutions, oversee testing and releases, apply patches, monitor systems, prevent downtime, and provide technical guidance. The role also involves end-user training, collaboration with global IT teams, software project management, Agile development, and adoption of AI-powered tools to improve delivery and operational efficiency.

Designs and executes NAND functional and design validation, characterization, simulation, and silicon debug. Develops test algorithms, random sequence generators, and automation frameworks using C/C++, Python, and Perl. Performs electrical failure analysis, root-cause investigations, and cross-functional issue resolution. Uses Nextest ATE, Verilog, Synopsys VCS, data analytics, dashboards, and AI/ML techniques to improve coverage, failure triage, test efficiency, and release readiness.

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.

Leads 3D NAND process integration and module development from R&D through high-volume manufacturing. Defines specifications, resolves structural, electrical, and reliability issues, optimizes process control and manufacturability, evaluates materials, conducts experiments, analyzes data, coordinates cross-functional teams, and guides technology transfer. The role also provides technical leadership, sets goals and strategy, drives innovation, and generates intellectual property.

Develop and validate an AI-driven root cause analysis tool for semiconductor Chemical Vapor Deposition incident data. The intern will prepare and classify equipment alarms, process-control records, and human-written incident logs; train machine learning or generative AI models; identify recurring failure patterns; conduct correlation studies; and prototype Microsoft Teams integration for incident validation. The role includes data visualization, model validation, root cause analysis, and collaboration with cross-functional engineering experts.

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.

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.