Top Tech Jobs & Startup Jobs in Singapore

The Engineer will support RAM operations, optimize tool performance, manage lot disposition, train team members, and enhance processes through data analysis.

Support development, characterization, and qualification of semiconductor PVD/CVD/PCVD tools. Run experiments, collect and analyze equipment data, document requirements and changes, participate in builds/installs/upgrades, collaborate with vendors and cross-functional teams, prepare technical reports, and follow safety and cleanroom protocols.

Lead wet-process and chemistry pathfinding for next-generation NAND: develop advanced wet etch, clean, and surface modification solutions, apply AI/ML and analytics, partner with suppliers and research collaborators, integrate across process teams, and mentor engineers.

The role involves product development and validation for HBM products, optimizing test coverage, improving yields, supporting new designs, and collaborating with cross-functional teams.

Prepare HBM component validation for silicon, identify and validate test equipment and flows, debug failures and implement fixes, collaborate with DV/QA/manufacturing/marketing, develop and validate test programs, analyze data and datasheets, present findings, and provide technical training.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

As a Product Engineer, you will drive NAND product development, ensuring quality and yield improvement through testing, analysis, and collaboration across functions.

This role involves setting up, maintaining, and troubleshooting process control systems at Micron, ensuring adherence to standards and coaching users in their applications.

Student internship in Process Integration focusing on NAND CMOS metallisation and reliability studies. Learn failure mechanisms, use Micron analytics (SMAI) and AI/ML platforms to analyze failure trends, contribute preliminary fail-model observations, and propose conceptual process improvements under team guidance.

Develop end-to-end data science and ML solutions for semiconductor manufacturing to improve yield, quality, cycle time, and reduce cost. Work across the full pipeline from data acquisition and cleansing through model building, deployment, visualization, and collaboration with international engineering teams. Leverage statistical modeling, deep learning, generative AI techniques, and manufacturing domain knowledge to drive high-impact projects.