Top Machine Learning Jobs in Singapore

Leads operational risk and control reviews for client onboarding and documentation processes. Responsibilities include data-driven testing, risk monitoring, root-cause analysis, remediation, control automation, and AI governance assessments covering data quality, bias, security, accuracy, and failure modes. Partners with compliance, legal, audit, data, and onboarding teams to strengthen governance, meet AML/KYC expectations, and improve control standards.

Develops AI-enabled planning models, predictive analytics, data pipelines, dashboards, and decision-support systems for semiconductor NPI, qualification, capacity planning, wafer allocation, and product ramp readiness. Partners with Fab Planning, S&OP, and Technology Development teams to evaluate scenarios, optimize engineering resources, incorporate yield and technology-readiness insights, automate workflows, and present strategic recommendations to leadership.

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

Leads strategic yield initiatives across DRAM, NAND, and HBM; benchmarks and models yield across fabs; defines and deploys engineering best-known methods; advances AI and digital transformation of yield workflows; and provides technical leadership across fabrication, technology development, product engineering, quality, and yield engineering teams.

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.

Develop advanced analytics solutions for High Bandwidth Memory manufacturing. Analyze engineering datasets, build automated data pipelines and monitoring workflows, apply statistical and machine learning methods to detect deviations, investigate process-to-product relationships, and create dashboards that improve yield, quality, reliability, and performance insights. The intern will present findings to engineering stakeholders and gain experience in semiconductor product engineering, manufacturing analytics, and AI-enabled engineering solutions.

Performs electrical device characterization, fault isolation, and physical failure analysis for Micron NAND products. The role identifies electrical and process failure mechanisms, supports product qualifications and new device introductions, collaborates with engineering and quality teams, presents findings, develops analysis techniques, and maintains failure-analysis equipment. It also incorporates AI-assisted tools, data analytics, machine learning, and automation to improve semiconductor yield and engineering effectiveness. Planned shift rotations may support manufacturing events and technology ramps.

Owns AI-driven cost-reduction solutions across procurement, facilities, construction, and tool-install processes. Partners with data scientists and engineers to identify use cases, develop business cases, prioritize projects, coordinate global implementation, and measure financial and operational impact. Requires strong analytical, communication, relationship management, and cross-functional leadership skills, with experience in cost initiatives within operations or procurement, preferably in semiconductor construction and tool-install environments.

Develops AI-enabled planning, predictive analytics, and decision-support solutions for semiconductor new product introduction. The role optimizes engineering demand, wafer allocation, qualification schedules, manufacturing capacity, yield assumptions, and product ramp readiness. Responsibilities include building planning models, data pipelines, dashboards, intelligent workflows, and scenario analyses while collaborating with Fab Planning, S&OP, Technology Development, and engineering leadership.

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

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.

Develops semiconductor memory reliability test flows, qualification plans, and manufacturing test strategies for HBM products. The role investigates reliability and RMA failures using electrical and physical failure analysis, drives root-cause resolution, supports process conversions, manages technical risks, and collaborates across fab, design, technology, systems, and quality teams. It also applies machine learning and AI automation to reliability data and workflows.

Leads global data science and analytics teams supporting semiconductor manufacturing. The role applies statistical modeling, machine learning, data engineering, and visualization to optimize NAND process capability, device yield, and operational performance. Responsibilities include building data pipelines, analyzing manufacturing and equipment data, designing experiments, developing automated tools, mentoring technical talent, and managing cross-functional analytics programs. Strong semiconductor manufacturing expertise, Python, SQL, cloud platforms, and advanced engineering analytics experience are required.

Conduct a semiconductor process-engineering project focused on wafer thermal management and plasma strip optimization. Plan experiments and Design of Experiments studies, analyze manufacturing data using statistics, process modeling, visualization, and AI-enabled workflows, and develop predictive analytics linking chuck temperature to process performance. Deliver thermal-characterization findings, optimization recommendations, and a technical presentation aimed at improving process robustness, yield, productivity, and cost performance.

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.

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

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.

Supports a five-month engineering project improving solid-state drive product development workflows. Responsibilities include analyzing engineering processes, developing Python scripts, SQL data pipelines, dashboards, automation solutions, and visualizations, and evaluating their effectiveness. The intern collaborates with engineering and cross-functional stakeholders to define requirements, validate solutions, document findings, and present recommendations. The role provides exposure to semiconductor manufacturing, engineering data analysis, workflow automation, and potentially AI-enabled information retrieval.

Develop AI Agent prototypes and Python automation solutions to improve productivity in semiconductor photo engineering workflows. Responsibilities include analyzing engineering data, identifying automation opportunities, building data-processing pipelines, creating dashboards, evaluating solution effectiveness, documenting designs, and presenting technical findings. The intern will gain experience with generative AI, large language models, data analytics, and semiconductor manufacturing engineering.

As a Principal Engineer, you will lead a team in defect detection and process improvement in the semiconductor industry, collaborating with various departments to drive innovation and mentor team members.

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.

Conduct ML research (deep learning, NLP, statistics) on large and unstructured datasets, implement algorithms in production-quality code, back-test models, and document findings to inform trading strategies.

Develop an AI-enabled optimization engine for wafer test movement configurations, using machine learning and optimization techniques. Analyze historical semiconductor data, benchmark recommendations, build a web application for visualization and comparison, and validate improvements in test time and tester utilization. The role involves collaboration with Automation, Equipment, and Engineering teams and requires a full-time commitment of at least five months.

Develops semiconductor memory product reliability test flows, qualification plans, manufacturing test strategies, and root-cause solutions for HBM products. Drives defect and reliability improvements, process conversions, yield, quality, cost, and cycle-time goals through cross-functional collaboration. Applies Python, JMP, machine learning, and AI tools to reliability data and workflows while supporting risk management, technical decisions, and team innovation.

Develops, optimizes, characterizes, and qualifies photolithography processes for advanced NAND nodes. The role applies experimentation, process modeling, DOE, AI, machine learning, and advanced analytics to improve yield, defectivity, overlay, critical dimensions, reliability, and process windows. Responsibilities include evaluating lithography equipment and materials, supporting technology transfer and manufacturing ramp, resolving integration challenges, collaborating globally, communicating technical findings, and contributing to patents and technical methodologies.