Top Machine Learning Jobs in Singapore

Intern will analyze NAND wafer probe and device-level test data, evaluate yield trends and failure signatures, optimize test coverage and efficiency, and develop AI-enabled analytics or automation solutions. Responsibilities include statistical analysis, anomaly detection, data visualization, technical reviews, and collaboration with design, manufacturing, reliability, and test engineering teams. The intern will deliver technical recommendations, an analytical prototype or workflow, and a final report and presentation.

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.

Supports technology and cybersecurity audits across Asia Pacific by assessing risks and controls, conducting walkthroughs and testing, evaluating control effectiveness, drafting reports, validating remediation, and monitoring technology programs. The role requires strong audit judgment, data analytics, stakeholder management, regulatory awareness, and communication skills, with responsibility for producing high-quality workpapers and recommendations while staying current on emerging technologies and regional requirements.

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.

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.

Conduct ML research (deep learning, NLP) on large and unstructured financial datasets, implement algorithms in high-quality code, back-test models, document findings, and collaborate with senior researchers during an 11-week summer internship in Hong Kong or Singapore.

Explore AI/ML-assisted and self-adaptive memory design techniques for optimizing performance, power efficiency, robustness, and yield. Analyze memory assist methods, operating conditions, failure signatures, and process variations. Develop adaptive monitoring, prediction, voltage, timing, and read/write control techniques, then document, benchmark, and present the proposed architecture and design insights.

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.

An off-cycle quantitative trading and research internship focused on derivatives pricing, risk management, electronic execution, systematic trading, and portfolio optimization. The intern will build mathematical and machine learning models, develop pricing and hedging tools, research trading strategies, conduct scenario analysis, assess model risk, and implement high-performance analytics in production systems while collaborating with traders, risk managers, and banking teams.

Three-to-six-month quantitative trading and research internship developing mathematical models, pricing and risk tools, algorithmic trading strategies, portfolio analytics, and high-performance software. Interns apply machine learning and advanced statistics to derivatives pricing, hedging, electronic execution, market making, risk management, and systematic trading. The role involves quantitative research, scenario analysis, model validation, production implementation, collaboration with traders and risk managers, and communicating technical findings.

Conduct ML/DL research to develop predictive models from large market datasets, integrate models into systematic trading strategies, contribute to AI/ML libraries, and deploy production algo systems with P&L responsibility across FX, Rates, Commodities, and Credit.

Summer analysts will build and deploy machine learning, generative AI, and agent-based solutions; integrate complex datasets; develop predictive models; run experiments; analyze trends; and deliver actionable insights. They will also support data governance, risk management, data quality, privacy, and regulatory controls while collaborating with agile, cross-functional teams. The program includes mentorship, training, and potential full-time opportunities after graduation.

Lead AI/ML risk and control activities across APAC to strengthen governance, operational risk management, and regulatory compliance. Partner with business and control stakeholders to define controls, support testing and issue remediation, complete risk assessments, maintain control documentation, analyze metrics for emerging risks, and prepare materials for regulatory exams.

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.

Analyzes logistics performance, costs, inventory flows, and service levels; develops dashboards and reports; leads global logistics transformation projects; implements AI, automation, and workflow improvements; manages SAP and logistics systems; coordinates testing, upgrades, training, governance, documentation, and continuous improvement initiatives.

Develop an AI-enabled automation solution for low-yield analysis in semiconductor memory testing. The intern will study test operations, manufacturing systems, test methodologies, data flows, and lot-disposition processes; apply programming, analytics, visualization, and AI tools; validate improvements in efficiency, accuracy, and consistency; and document findings through a final report and presentation.

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.

Owns pre-silicon validation, simulation regression, post-silicon characterization, and simulation-to-silicon correlation for NAND test chip vehicles. Analyzes device and architecture sensitivities, defines test methodologies, leads root-cause investigations, and drives silicon learning across design, process, technology, and test teams. The role also develops reusable technical methods, influences cross-functional decisions, mentors engineers, and applies data analytics, machine learning, and AI-assisted tools.

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.

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 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.

Supports advanced copper sputter process development for HBM semiconductor metallization. Responsibilities include process characterization, hardware qualification, integration assessment, statistical and machine-learning analysis, defect reduction, yield improvement, and manufacturing readiness. The intern will use AI, manufacturing systems, and data analytics to identify process trends and recommendations while collaborating with process integration, manufacturing, equipment, and quality teams.

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.

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.

Develop an end-to-end analytics and machine learning solution for semiconductor manufacturing data. Analyze electrical, physical, inline, and probe measurements; perform data querying, preprocessing, feature engineering, modeling, optimization, and visualization; and create predictive models and drift-detection methods to improve production-line monitoring and engineering decisions. The intern will document and present findings while collaborating with manufacturing engineering teams.