Top Machine Learning Jobs in Singapore

Manage end-to-end delivery of IT and software solution projects for government and enterprise clients. Responsibilities include planning schedules and budgets, managing risks, resources, vendors, contracts, stakeholders, governance, quality, and project financials. The role leads cross-functional technical teams and may use Agile, Waterfall, or hybrid methods across cloud, enterprise application, data, AI/ML, and low-code projects.

Drives semiconductor diffusion process and equipment performance, productivity, capacity, yield, reliability, and cost improvements. Leads technical investigations, bottleneck reduction, supplier engagement, technology transfers, and cross-functional engineering projects. Uses data analytics, AI, machine learning, statistical methods, automation, and predictive maintenance approaches to improve manufacturing operations. Develops best practices, supports new capabilities, provides technical recommendations, and mentors junior engineers.

Develop data sources and automated collection processes, process structured and unstructured data, identify trends, build predictive and machine learning models, visualize findings, and recommend business solutions. The role also involves evaluating statistical and AI models, facilitating data design workshops, and collaborating with stakeholders, particularly in banking and insurance.

Conducts advanced AI research across foundation models, deep learning, reinforcement learning, computer vision, natural language understanding, and industrial AI. Develops algorithms, prototypes, datasets, ETL pipelines, and machine learning models; evaluates and monitors model performance; collaborates with product and engineering teams on technology transfer and production applications. Publishes research, files patents, documents findings, and advances Microsoft’s research agenda.

Develop and deploy machine learning models, predictive analytics, and agentic AI solutions for semiconductor manufacturing and engineering. Build scalable data pipelines and ML workflows using large-scale manufacturing datasets to improve yield, quality, reliability, cycle time, and cost. Integrate AI agents with enterprise platforms, mentor Citizen Data Scientists, lead technical initiatives, and support production deployment, MLOps, innovation, and AI adoption across Product Engineering.

Build, deploy, and maintain MLOps pipelines and agentic AI components supporting continuous risk assessments and auditing. Collaborate with audit, data, and engineering teams to identify AI use cases, develop scalable production solutions, evaluate model performance, and monitor reliability. The role also maintains AI component libraries, introduces MLOps innovations, troubleshoots deployments, and communicates technical solutions to stakeholders.

Develop and deploy predictive models, analyze large datasets, and generate insights for business decisions. Collaborate with stakeholders to create data-driven solutions, design visualizations, and communicate findings. Architect, scale, and maintain reusable AI services and generative AI applications while staying current with data science, machine learning, and generative AI developments.

Serves as the bridge between business functions and Technology & Operations by coordinating infrastructure initiatives, gathering requirements, managing vendors, overseeing budgets, supporting governance, and tracking technology project performance. The role partners with stakeholders to resolve operational issues, translate business needs into IT requirements, coordinate UAT and deployments, develop business cases, and align technology investments with strategic priorities.

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 casino operations, product and area performance, manpower efficiency, and dealer performance. Apply advanced analytics, modeling, and machine learning to optimize gaming floors, forecast demand, schedule manpower, and evaluate risk. Build dashboards, reports, analytical tools, and A/B tests; translate insights into business strategies; evaluate revenue impacts; and collaborate with Operations, Marketing, BI, data warehouse, IT, and smart IR teams.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Build and deploy engineering-grade digital models for Japanese utility clients by integrating GIS, LiDAR, and other data sources. Configure platform projects, create dashboards, resolve model inaccuracies, support technical delivery and pre-sales, and collaborate with product and engineering teams on platform improvements and Japanese localization. The role requires customer-facing communication in Japanese, global collaboration in English, mentoring, and occasional travel to customers and international offices.

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.

Assist R&D on simulation projects for additive manufacturing and design optimization. Perform FEA and CFD, validate simulations with experiments, collaborate with product teams, and present engineering solutions under senior supervision.

Develops Scala, Spark, and PySpark data transformation and aggregation jobs for risk platforms. Builds Spark and Spark SQL applications using Hadoop, Hive, and distributed processing technologies; writes unit tests, performs code and peer reviews, documents solutions, and supports production deployments. Collaborates with business analysts on testing and sign-off while maintaining code quality, automation, and delivery timelines.

Lead AI and machine learning projects, develop production LLM and agentic applications, mentor apprentices, maintain AI technologies, and support AI training programmes. Responsibilities include end-to-end project delivery, use-case scoping, applied machine learning, MLOps, cloud pipelines, technical research, curriculum development, workshops, and communicating complex concepts to technical and non-technical audiences.

Editorial guide comparing AI-powered payment fraud detection and AML compliance software. It reviews tools including Stripe Radar, Forter, Sift, Kount, Signifyd, Riskified, Sardine, Fraudio, ClearSale, and Feedzai, explaining their use cases, fraud signals, pricing models, implementation considerations, chargeback protection, and selection criteria for merchants, fintechs, PSPs, acquirers, and financial institutions.

Develop and deploy AI solutions for public- and private-sector clients while supporting AI governance, data protection, fairness, and transparency. Apply machine learning across NLP, computer vision, recommendation systems, reinforcement learning, and forecasting. Build end-to-end AI systems using modern cloud, MLOps, software engineering, and infrastructure tools. Collaborate with clients, colleagues, and partners, continuously expand technical expertise, and contribute to transformative digital initiatives.

Designs and deploys AI solutions including generative AI copilots, recommendation systems, RAG, and agentic AI. Builds predictive and generative models using structured and unstructured data, applies advanced machine learning techniques, develops data visualizations and stories, and partners with cross-functional teams on cloud, MLOps, and real-time data platforms. May manage a team depending on seniority.

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.

Lead Waymo’s autonomous ride-hailing service enablement and rollout in Singapore. Define the local service expansion roadmap, understand rider and market requirements, coordinate with strategy, business development, product, operations, engineering, and external partners, and resolve local product challenges. The role also supports product and service integration with local partners and feeds market insights into broader product development.

Oversee regional financial health through budgeting, forecasting, month-end close, accruals, variance analysis, management reporting, KPI tracking, and cost-saving initiatives. Partner with operations and departments to improve efficiency, support strategic decisions, and ensure policy compliance. Develop financial models, apply business intelligence and machine learning to analyze data, and provide actionable insights. Participate in cross-functional projects and support headquarters’ controlling and accounting inquiries.

Summer interns build data pipelines, analyze market data, develop models and trading signals, integrate AI and LLM tools, and contribute to analytics used for research, trading, and risk management. The program includes financial markets training, mentoring, and exposure to macro investing. Candidates should have strong quantitative, technical, problem-solving, communication, and teamwork skills, plus an interest in trading and financial products.