Top Machine Learning Jobs in Singapore

Deploys, configures, optimizes, and supports CrowdStrike Falcon’s AI-powered detection and response capabilities. The role develops automation playbooks and API integrations, tunes machine-learning detection models, troubleshoots customer issues, supports threat hunting and forensic analysis, integrates security workflows, and leads AI workshops. It also requires customer engagement, technical presentations, project prioritization, cross-functional leadership, and mentoring while applying cybersecurity, AI/ML, and platform deployment expertise.

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.

Full-time internship focused on applying Python, statistical analysis, visualization, and introductory machine learning to semiconductor probe-test, electrical failure, and fabrication-process data. The intern will investigate yield behavior in advanced memory manufacturing, develop a multivariate analytics tool, identify potential yield-improvement opportunities, and present documented findings through a technical report and presentation.

Intern will develop and deploy a digital Target Traveler solution for semiconductor Probe operations. Responsibilities include analyzing process flows, defining control requirements, configuring workflows, creating data collection and compliance dashboards, monitoring deviations, and recommending process improvements. The role involves collaboration with engineering, manufacturing, quality, and IT teams while building experience in semiconductor process control, analytics, workflow automation, digital transformation, and AI-enabled manufacturing.

Intern will analyze Wafer Intelligent Scanner, defect, yield, process, and equipment data to identify abnormal wafer bevel behavior and early indicators of defects. Responsibilities include data preparation, statistical and machine learning analysis, model evaluation, dashboard creation, and technical reporting. The project supports semiconductor yield improvement and defect reduction through AI-enabled manufacturing analytics.

Five-month internship focused on applying data analytics and machine learning to semiconductor manufacturing data. The intern will query and preprocess large-scale quality datasets, develop models for quality-metric drift detection, perform multivariable analysis and optimization, create visualizations, and automate engineering workflows. Responsibilities include collaborating with engineering teams, documenting results, and presenting recommendations. Relevant backgrounds include engineering, statistics, mathematics, computer science, data science, or another analytics-related discipline.

Develops and optimizes semiconductor process control systems to improve yield, quality, reliability, cost, and productivity. Responsibilities include data analysis, process modeling, statistical and fault detection methods, equipment and material evaluation, control limit and OCAP development, supplier coordination, system integration, and smart manufacturing solutions using machine learning, IIoT, and advanced controls. The role also provides technical support, training, and cross-functional collaboration across process and equipment teams.

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.

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.

Analyzes logistics performance, transportation costs, inventory flows, service levels, and operational KPIs. Leads global logistics transformation projects, develops dashboards and reports, identifies cost-saving opportunities, and implements AI, automation, and digital workflow solutions. Manages logistics systems including SAP and transportation platforms, gathers requirements, coordinates testing and deployments, maintains documentation, supports training, and drives process standardization, governance, continuous improvement, and logistics network optimization.

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 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.

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.

Leads governance, compliance, continuous improvement, and digital transformation for global frontend capital management processes. Responsibilities include capital forecasting, funding and savings cycles, reporting, process standardization, dashboard development, analytics, automation, and cross-functional program leadership. Partners with planning, finance, procurement, facilities, and business stakeholders to improve data quality, operational efficiency, adoption, and decision-making.

Conduct silicon design validation for advanced NAND products through Verilog simulations, validation planning, laboratory measurements, and data analysis. The intern will use testers, oscilloscopes, probe stations, scripting, and engineering software to analyze validation data, generate technical reports, and prototype AI-enabled workflows for automated data collection, reporting, or anomaly detection.

Develop an IoT-based monitoring and anomaly-detection solution for photolithography equipment in a semiconductor manufacturing environment. The intern will study equipment behavior and sensor signals, build prototypes for data ingestion, processing, analytics, and visualization, and apply Python, machine learning, and signal-processing techniques. Responsibilities include integrating equipment or process data, analyzing imaging, acoustic, environmental, motor, or material-usage data, documenting results, and presenting a final technical report.

Intern will develop AI-enabled analytics, automation workflows, dashboards, and software solutions for NAND semiconductor test engineering. Responsibilities include analyzing manufacturing and test datasets, automating test-result analysis and validation, correlating wafer yields, exploring machine learning for predictive insights, and improving test quality, productivity, and engineering cycle time. The role provides hands-on exposure to wafer probe, burn-in, assembly testing, and high-volume semiconductor manufacturing while collaborating with engineering, manufacturing, quality, and data science teams.

The Engineer CE PYE will perform fault isolation, electrical device characterization, and physical failure analysis, collaborating with product teams to improve defectivity and operational efficiency.

Contribute to semiconductor manufacturing EHS projects involving compliance tracking, risk monitoring, environmental compliance, safety initiatives, data analysis, dashboards, automation workflows, and AI-enabled solutions. The intern will complete an improvement project, develop recommendations and an implementation plan, and present findings. The five-month internship provides practical experience with ISO 14001, ISO 45001, workplace safety, risk assessment, digitalization, and continuous improvement.

Supports a five-month product engineering project involving NAND Flash, memory products, and SSDs. Analyzes test coverage, yield data, electrical measurements, and failure mechanisms; conducts circuit simulations and engineering evaluations; and develops scripts, dashboards, automation, or AI-enabled workflows. The intern documents findings, recommends improvements to product quality and test efficiency, and presents a final technical report under experienced engineering guidance.

Designs and deploys end-to-end data science solutions using large, diverse datasets. Responsibilities include building data pipelines, developing machine learning models, analyzing semiconductor yield and process data, extracting insights with SQL, deploying production AI solutions on GCP, and communicating findings to technical and business stakeholders. The role also involves model monitoring, maintenance, experimentation, and collaboration with product and service teams.

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.

Five-month internship developing AI and machine learning solutions for semiconductor facilities engineering. Responsibilities include analyzing operational and sensor data, building predictive maintenance and anomaly-detection prototypes, creating dashboards and automation workflows, and developing AI-enabled knowledge-management tools. The intern will collaborate with facilities engineers, apply software-engineering principles, document findings, and present recommendations and business value.

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.

Develop an AI-enabled application, analytical model, or automated workflow addressing a semiconductor process or equipment engineering challenge. Analyze manufacturing and engineering data using statistics, machine learning, Generative AI, large language models, visualization, or automation. Test, validate, document, and present the solution, demonstrating potential improvements in yield, quality, cost, productivity, or cycle time.