Top Machine Learning Jobs in Singapore

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.

Intern will develop software, data pipelines, integrations, dashboards, automation, and AI-enabled solutions for photolithography engineering in semiconductor manufacturing. Responsibilities include connecting equipment, manufacturing systems, databases, and engineering platforms; applying analytics and machine learning; and using modern software practices such as version control, testing, deployment, and maintainable architecture. The intern will collaborate with multidisciplinary engineering teams and deliver production-quality applications, documentation, reusable code, and a final technical presentation.

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.

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.

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.

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.

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.

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.

Conduct experiments to improve poly-SiGe gap filling in high-aspect-ratio semiconductor structures. Analyze deposition effects on film growth, conformality, voids, seams, pinch-off, and structural integrity using DOE, SEM/TEM cross-sections, and statistical methods. Correlate fill quality with die strength and wafer or block bending, then recommend optimized process windows and qualification plans. Collaborate with engineering teams and subject matter experts throughout the development cycle.

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.

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.

Lead global analytics engineering initiatives for electronic trading and research. Design, build, and optimize real-time data pipelines, scalable APIs, analytics libraries, and production trading tools. Apply AI to data engineering, analytics, and SDLC automation while ensuring reliability, testing, security, and continuous delivery. Collaborate with trading and research teams, onboard datasets, mentor engineers, manage workstreams, and establish technical standards across distributed teams.

Lead and grow a team of CMP and BTG engineers to develop and transfer CMP/BTG process modules for 3D NAND. Drive process characterization, DOE-based optimization, process margin and manufacturability improvements, supplier partnerships, pilot ramp/yield support, and adoption of data analytics/AI for predictive modeling and process control.

Leads smart manufacturing data science initiatives for the F10 organization, serving as the bridge between site leadership, users, and enterprise teams. Responsibilities include prioritizing requirements, driving solution adoption, balancing local support with network best-practice deployment, evaluating capabilities, coordinating cross-functional projects, monitoring deliverables, and managing competing resources and priorities.

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.

Engineers at Citadel work in small teams to develop high-performing technology under quick cycles. They focus on creating tools for trading strategies and data research platforms.

Summer intern role building high-performance, large-data research platforms and web frameworks. Collaborate in small teams to create tools for trading strategies, applying distributed computing, NLP, and machine learning.