Top Machine Learning Jobs in Singapore

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.

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.

Leads HBM product reliability engineering, including reliability test-flow development, qualification execution, DPM reduction, failure analysis, process conversions, risk management, and fab improvements. Partners cross-functionally to resolve qualification and RMA issues while optimizing quality, cost, yield, and cycle time. Also develops AI and machine-learning applications for reliability data, promotes AI adoption, and mentors engineering teams.

Develops semiconductor product reliability test flows, qualification plans, and manufacturing test strategies for HBM products. Leads DPM reduction, failure analysis, root-cause resolution, process conversions, risk management, and high-volume manufacturing support. Collaborates with fab, design, technology development, systems, and quality teams. Applies Python, JMP, machine learning, and AI tools to reliability datasets and workflows while making technical decisions and mentoring others.

Lead HBM product reliability engineering, including stress-test flow development, qualification execution, DPM reduction, failure analysis, process conversion, and fab yield improvements. Manage reliability risks, cross-functional projects, technical decisions, and team mentorship. Apply machine learning and AI tools to reliability data, identify failure patterns, and embed AI-driven automation into qualification and engineering workflows.

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.

Develop AI-enabled automation workflows for NAND device characterization and engineering data analysis. Build data pipelines, machine learning models, anomaly detection tools, dashboards, AI assistants, and automated reporting solutions using large-scale semiconductor datasets. The role involves applying data science, generative AI, LLMs, and agentic AI to improve engineering productivity and decision-making while collaborating with cross-functional engineering teams.

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.

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.

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 infrastructure platform architecture across cloud-based technologies, translating complex technical issues for leadership, evaluating emerging technologies, improving governance, and guiding engineering teams. Develops reusable AI-enabled engineering patterns with validation, security, resiliency, and auditability controls. Partners with stakeholders, vendors, and technical teams to align local solutions with strategic architecture targets and influence product design, functionality, and technical operations.

Lead quantitative analysis, experimentation, and statistical modeling to guide product and business decisions. Build scalable analytics systems and decision frameworks, partner with cross-functional teams, define success metrics, and measure initiative impact. Communicate insights to executives, influence product strategy and roadmaps, and solve ambiguous problems using large datasets. The role also contributes to machine learning, AI, experimentation, and production decision-support systems.

Conduct quantitative research for investment strategies using advanced statistical and mathematical modeling, machine learning, data analysis, portfolio optimization, and algorithm development. Build predictive models and analyze price formation, risk, and financial market drivers. Collaborate with researchers, engineers, and traders to develop scalable systems and solve real-world investment problems.

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.

Leads semiconductor manufacturing and equipment capability roadmaps, supplier technology partnerships, global manufacturing strategy, and cross-site productivity initiatives. Improves CVD equipment performance, reliability, throughput, utilization, cycle time, and cost efficiency through continuous improvement, root-cause analysis, technology transfer, and process qualification. Applies AI, machine learning, advanced analytics, and digital manufacturing solutions to optimize equipment and manufacturing operations while mentoring engineers.

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.

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 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.

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.

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.

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.

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.

Owns the identification, prioritization, and implementation of AI solutions that reduce costs across procurement, manufacturing, planning, and program management. Collaborates with data scientists, data engineers, and global business teams to develop business cases, validate ROI, monitor financial and operational impact, and establish new AI use cases. Requires strong cost-project experience, analytical ability, communication skills, and cross-functional leadership, preferably in the semiconductor industry.

Owns AI-driven cost-reduction products and use cases across procurement, operations, engineering, and commercial functions. Collaborates with data scientists, data engineers, and global teams to implement solutions, quantify financial and operational impact, develop ROI-based business cases, prioritize projects, and identify additional opportunities. Requires strong cost-project experience, analytical skills, cross-functional leadership, communication, and relationship management.