Top Machine Learning Jobs in Singapore

Leads complex technology consulting engagements for enterprise, government, and service-provider customers. Designs and implements Red Hat OpenShift, Kubernetes, MLOps, generative AI, and agentic AI architectures across hybrid and multi-cloud environments. Delivers workshops, advises stakeholders, supports pre-sales, manages project delivery, and mentors consultants. Establishes platform best practices for security, observability, scalability, resiliency, governance, and AI lifecycle management while contributing to consulting methodologies and reference architectures.

Design, build, and deploy production agentic applications for semiconductor metrology. Integrate AI agents with systems, data sources, services, and user interfaces while ensuring reliability through testing, CI/CD, monitoring, security, and quality practices. Collaborate with engineering teams on architecture and implementation, support fab and customer deployments, conduct reviews, mentor engineers, and improve software processes. The role requires production LLM application experience, strong software engineering skills, and proficiency in Python, C++, or C#.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research on financial markets by discovering predictive patterns, developing statistical and machine learning models, running large-scale simulations and backtests, and translating research into production-ready trading signals. Interns collaborate with researchers and technologists, build research infrastructure and AI-powered agents, and complete rigorous projects from hypothesis through evaluation under realistic market conditions.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Manages end-to-end procurement for capital equipment, spares, and services, including P2P processes, supplier relationships, negotiations, contracts, delivery, cost optimization, and performance management. Drives localization, supply-chain resilience, continuous improvement, and AI-enabled procurement initiatives. Partners with Procurement, Engineering, Planning, Finance, and Legal teams while providing strategic recommendations and executive-level supplier insights.

Leads operations research model application, optimization, simulation, scenario analysis, and decision support for network planning. Defines requirements and constraints, translates model outputs into recommendations, prioritizes opportunities, measures business value, and drives adoption. Leads cross-functional analytics initiatives, supports AI-enabled solutions in GCP, and communicates insights to stakeholders. Requires strong SQL, Python, optimization, scheduling, simulation, resource allocation, business planning, and leadership capabilities.

Provides advanced applications support for semiconductor metrology products through customer demos, evaluations, onsite technical support, product qualification, solution optimization, training, and technical documentation. Develops metrology solutions involving thin-film modeling, optical critical-dimension modeling, and machine learning for semiconductor devices. Collaborates with customers and internal teams, troubleshoots technical issues, publishes technical papers when needed, and travels internationally up to 60% of the time.

Design and advance manufacturing solutions for advanced semiconductor inspection and metrology products. Collaborate with manufacturing, supply chain, installation, quality, global suppliers, and design engineering teams to develop scalable, efficient, and cost-effective electronics manufacturing processes and products.

Leads the strategy, architecture, roadmap, governance, and delivery of DBS’s in-house AdTech and performance marketing platform. Owns integrations, APIs, audience activation, measurement, AdOps controls, AI-enabled marketing capabilities, creative automation, and hybrid agency operating models. Partners with Marketing, Technology, Data, Analytics, Risk, Legal, Procurement, agencies, and media platforms to improve signal quality, automation, campaign performance, and measurable business outcomes across markets.

Develop and optimize machine learning and deep learning algorithms for intelligent surveillance products. Responsibilities include model training, evaluation, data preprocessing, feature engineering, algorithm research, computer vision tasks such as detection, tracking, recognition, and feature extraction, deployment across hardware platforms, documentation, and collaboration with technical teams.

University students will gain hands-on experience developing technology solutions and applying AI, data science, machine learning, deep learning, GenAI, and MLOps to banking challenges. Responsibilities may include building LLM applications, developing models, implementing responsible AI controls, supporting cloud and DevOps initiatives, testing, deployment, and collaborating with cross-functional teams. The internship requires a minimum 12-week full-time commitment and includes assessments and interviews.

Lead digital transformation and continuous improvement of the Housing Management System and related platforms. Manage vendor relationships, stakeholder engagement, change management, data analysis, automation and AI initiatives (Generative and Agentic AI, RPA), and governance of housing digital content to improve operational efficiency and service delivery.

Leads strategic technology initiatives and digital transformation through scalable, secure cloud-native and microservices architectures. Drives AI adoption, conducts technical assessments and proofs of concept, develops solution blueprints, evaluates vendors, and guides modernization. Partners with business, product, and engineering stakeholders to deliver transformation proposals and implementation architectures while ensuring security, governance, regulatory compliance, resiliency, and availability.

Lead APAC payments transformation initiatives from strategy through execution. Responsibilities include identifying growth and efficiency opportunities, conducting quantitative and root-cause analyses, developing business cases, managing project governance, optimizing processes and operating models, assessing regulatory risks, creating mitigation strategies, and coordinating cross-functional stakeholders to deliver scalable solutions.

Leads complex technical programs from concept through deployment, coordinating product, engineering, marketing, operations, UX, sales, and legal teams. Owns requirements, architecture, integration, release management, budgets, schedules, risks, compliance, demonstrations, pilots, and product launches. Facilitates Agile teams and Scrum Masters, supports go-to-market activities, communicates with senior leadership, and ensures delivery of quality solutions. The role also mentors program managers and applies software, cloud, mobile, and asset-intelligence development practices.

Lead regional data science and machine learning initiatives for financial crime surveillance, including AML networks, transaction monitoring, fraud prevention, money mule detection, and sanctions screening. Develop end-to-end models from problem formulation through deployment, monitoring, and support. Partner with business and technical stakeholders, ensure AI/ML governance and data quality, communicate insights, and mentor junior data scientists.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Lead governance for AI-enabled capabilities across their lifecycle, including risk classification, impact assessments, security and compliance controls, monitoring, audits, third-party reviews, and remediation. Develop AI governance frameworks, policies, lifecycle gates, risk registers, and management reporting. Partner with engineering, cybersecurity, data, product, operational, audit, and vendor stakeholders to implement secure, explainable, responsible, and proportionate AI controls aligned with IM8, ISO 27001, and recognized AI governance standards.

Build and own test automation, frameworks, test data, isolated environments, contract tests, and quality gates for risk decisioning systems. Integrate API, end-to-end, regression, smoke, and post-release verification testing into CI/CD. Validate jurisdiction-specific behavior, mentor engineers, support release acceptance, and develop testing approaches for ML models and LLM agents.

Lead and grow a Singapore-based enterprise sales team of 5–7 representatives. Responsibilities include recruiting and developing sales leaders, creating business plans, exceeding quarterly and annual targets, managing the sales pipeline from lead generation through closing, expanding enterprise accounts, navigating complex sales cycles, and developing go-to-market strategy with security channel partners.

Conduct research combining safety-critical control theory, nonlinear system control, and machine learning for robotic systems. Responsibilities include experimenting on robotic platforms, collaborating internationally, and publishing research in leading journals and conferences. Candidates need a relevant bachelor’s degree, familiarity with control theory, machine learning systems, and robotics, plus programming proficiency in Python and experience with tools such as PyTorch.

Build and scale Airwallex’s machine learning platform for risk and fraud decisioning. Design data and model infrastructure, productionize training and serving workflows, improve experimentation, and optimize GPU-based training and inference. Partner with machine learning engineers, data scientists, product managers, and risk specialists to deliver reliable, low-latency systems. The role is based in Singapore and requires working from the office five days per week.

Develop machine learning, statistical, optimization, computer vision, Generative AI, and advanced analytics solutions for smart manufacturing. Analyze operational data, deploy production-ready AI models, monitor performance, and collaborate with manufacturing, engineering, supply chain, quality, IT, and software teams to improve quality, productivity, reliability, and decision-making across global operations.

Lead AI governance across the banking group by reviewing AI use cases, conducting risk assessments, maintaining inventories and documentation, implementing lifecycle controls and guardrails, coordinating with validators, auditors, compliance teams, and regulators, monitoring emerging regulations, and supporting AI governance councils. The role focuses on responsible use of traditional, generative, and agentic AI while improving governance processes and operational efficiency.