Top Machine Learning Jobs in Singapore

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.

Leads High Bandwidth Memory product development from introduction through high-volume production. Owns technical program requirements, milestones, reviews, risk mitigation, decision-making, and cross-functional coordination across engineering, quality, supply chain, packaging, test, and manufacturing teams. Represents programs to executives, drives change management, and develops distributed team members through mentorship, coaching, evaluations, and accountability.

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.

Analyze semiconductor manufacturing and measurement data to identify cumulative error variation in staircase and chop dry etch processes. Apply statistical analysis, data visualization, and AI-enabled workflows to determine variation sources, develop process control strategies, and recommend improvements to process margins, inline stability, yield, efficiency, and equipment productivity. Collaborate with engineering teams and present findings to department leadership.

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.

Owns smart manufacturing and AI product initiatives focused on yield analysis and Industry 4.0 adoption. Responsibilities include diagnosing process gaps, defining requirements, standardizing scalable solutions, developing business cases and ROI analyses, prioritizing projects, coordinating global technical teams, and guiding system capabilities. The role partners with solution architects, data scientists, data engineers, and machine learning engineers while using performance metrics and AI-enabled tools to drive continuous improvement.

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.

Lead data science strategy for AML and financial crime risk controls. Develop AI-powered screening, entity resolution, graph and network intelligence, anomaly detection, transaction monitoring, and proactive discovery of emerging threats. Partner with Product, Engineering, Compliance, and Operations to deliver scalable production controls, influence the roadmap, establish reusable frameworks, and mentor data scientists.

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.

Develop and evaluate a Python-based automated framework for Non-Zero-Offset control in high-volume semiconductor photo processes. The intern will analyze inline, recipe, metrology, and historical process data; apply statistical and machine-learning methods; create visualizations and automation prototypes; and validate findings with engineering subject matter experts. Deliverables include cleaned datasets, reusable scripts, predictive or risk-identification models, technical documentation, and a final presentation.

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.

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.

Develop a prototype AI-enabled equipment health monitoring and reliability analytics framework. Analyze alarms, downtime, maintenance, and performance data; create health indicators; apply statistical and machine learning methods for anomaly detection and degradation analysis; build an intelligent monitoring agent and interactive dashboard; and document and present findings to engineering stakeholders.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Design, develop and roll out geospatial AI-centric MVPs and digital products. Work full-stack on frontend and backend, implement and fine-tune ML/GenAI/LLM models, use cloud and DevSecOps practices, collaborate with stakeholders in cross-functional teams.

Define and develop reliability stress tests and qualification plans for HBM products; drive DPM reduction, root-cause analysis (EFA/PFA), process conversions, risk management, cross-functional collaboration, and apply AI/ML tools to reliability datasets to improve test strategy and production yield.

Develop and optimize semiconductor assembly and packaging processes, characterize process windows, improve yield and reliability, and lead root-cause and failure-analysis investigations. Support NPI, product qualification, technology transfers, process readiness, and high-volume manufacturing. Use statistical methods, manufacturing datasets, visualization, machine learning, and AI-enabled monitoring to identify trends and optimize processes. Provide technical leadership, mentor engineers, and coordinate cross-functional teams across process development, manufacturing, yield, reliability, integration, and equipment engineering.

Develop and deploy machine learning models for influencer marketing, including creator discovery, content understanding, campaign prediction, and optimization. Clean and structure large datasets, engineer features, design experiments, integrate models into production APIs and inference workflows, and monitor performance. Collaborate with product, engineering, and business teams to translate ambiguous problems into scalable AI-powered product capabilities.

Analyze large-scale gaming datasets using statistics, machine learning, forecasting, recommender systems, generative AI, experimentation, causal inference, and econometrics. Collaborate with product and engineering teams to build data-driven solutions, deliver stakeholder insights, and support innovative gaming applications. Contribute to technical documentation, knowledge sharing, and cross-team collaboration.

Conduct research on machine learning-driven array-based nanosensors. Responsibilities include synthesizing and functionalizing receptor-coated nanoparticles, performing SERS and other spectroscopic measurements, building spectroscopic databases, analyzing receptor-analyte interactions using DFT simulations, applying machine learning to optimize receptor combinations, and supporting scientific publications.

Develop and scale AI, machine learning, and advanced analytics solutions for financial crime compliance. Responsibilities include building proof-of-concepts, production analytics pipelines, ML models, and agentic AI workflows; validating model performance and explainability; conducting risk surveillance; investigating anomalies; and communicating analytical insights to stakeholders. The role also supports the compliance innovation roadmap and enterprise adoption of successful AI initiatives.

Own the vision, strategy, roadmap, and lifecycle of a shared lending platform serving multiple products and markets. Define platform architecture principles, APIs, configuration capabilities, and build-versus-buy decisions. Lead real-time credit decisioning, risk and compliance integrations, data aggregation, IFRS9 reporting, and ML-assisted lending capabilities. Coordinate dependencies across lending products and collaborate with Credit Risk, Analytics, Engineering, Compliance, and country business teams.

Develop and optimize semiconductor metrology solutions for complex materials and device structures. Work with regional and international wafer fabrication customers on demonstrations, evaluations, product qualification, technical development, troubleshooting, documentation, training, and technical publications. Apply optical modeling, machine learning, statistics, and data analysis to technologies supporting 3D NAND, logic, and DRAM manufacturing.

Supports digital transformation and continuous improvement of chemical production through Smart Operations technologies. Implements, monitors, maintains, and improves digital applications; develops and executes process engineering studies and projects; manages technical changes, MOC activities, documentation, training, and project teams. Collaborates with production, R&D, site management, and global Smart Operations teams to pilot technologies, optimize performance, improve sustainability, and ensure compliance in methionine plant operations.

Lead regional sales operations planning and execution—territory, quota, segmentation, employee data, and Blueprint. Drive automation and AI adoption, deliver actionable insights and standardized reporting, strengthen governance, and partner cross-functionally to improve coverage, quota predictability, and operational efficiency.