Top Machine Learning Jobs in Singapore

Participate in meaningful business projects, collaborate with cross-functional teams, learn from cybersecurity and technology experts, and develop technical, analytical, business, and professional skills. Internship opportunities may span research and development, software engineering, AI and machine learning, and cybersecurity. Interns receive mentorship, networking opportunities, global exposure, and may be considered for future roles.

Conduct paleoseismic trenching, fault-zone and bedrock analysis, geological sample characterization, laboratory testing, scientific data management, and technical reporting. The Research Engineer II role develops and validates operational seismic-processing software, integrates AI-assisted earthquake monitoring workflows, contributes to research publications, and mentors students. Roles require multidisciplinary collaboration, detailed documentation, and field or laboratory work.

Design and optimize high-frequency trading infrastructure, including exchange connectivity, market-data systems, trading simulations, risk tools, and performance tracking. Develop low-latency network and systems software, analyze market data, and build tools for identifying trading patterns. Candidates should have a computer science degree, C++ development experience, strong data structures and algorithms knowledge, and familiarity with high-throughput systems, Linux, and networking.

Leads computational molecular design, cheminformatics, synthesis, characterization, and optimization of novel molecular constructs for next-generation sequencing. Drives Design-Make-Test-Learn workflows, develops predictive molecular models, performs peptide, oligonucleotide, linker, and conjugate synthesis, and collaborates across chemistry, biology, engineering, bioinformatics, and data science. The role also includes intellectual property development, technical leadership, mentoring, risk mitigation, and presenting scientific recommendations.

Lead operational risk data analytics and technology initiatives, including machine learning and GenAI applications. Define project scope, plans, requirements, user stories, and system enhancements; coordinate stakeholders and technology teams; conduct user acceptance testing; resolve production issues; track delivery risks; maintain documentation; and train users on system changes.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Designs production-ready enterprise AI and Kubernetes architectures, leads customer workshops and proofs of concept, advises executives, integrates technology partners, and influences product roadmaps. The role requires strong Kubernetes, Python, automation, AI/ML platform, MLOps, observability, GPU infrastructure, security, and regulated deployment expertise, along with exceptional technical communication and customer consulting skills.

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.

Own the vision, roadmap, prioritization, and delivery of Prudential Financial Advisers’ digital, data, automation, and AI-enabled products. Translate business needs into product requirements, backlogs, user journeys, acceptance criteria, and measurable outcomes. Coordinate Agile delivery, stakeholder alignment, risks, budgets, vendors, operational readiness, and reporting while guiding cross-functional technology teams. The role also evaluates AI and automation opportunities and manages products such as the PFA website and PRUEasy Quote.

Validate traditional machine learning, generative AI, and agentic AI systems across their lifecycle. Design evaluation methodologies, test model performance and guardrails, assess risks including bias and explainability, and document regulatory-grade findings. Develop AI validation standards and reusable testing assets, advise Risk, Technology, Audit, and business stakeholders, and monitor evolving AI technologies, regulations, and industry practices.

Deliver data science consulting projects for marketing, customer, and product use cases in luxury, consumer goods, and retail. Responsibilities include data analysis, KPI design, experimentation, machine learning, generative AI, data engineering, visualization, stakeholder storytelling, and solution deployment. The role also supports transformation, training, thought leadership, R&D, and business development.

Leads APAC data strategy and Greater China AI strategy initiatives across media campaigns. Develops audience segments from first-party and external data, drives adoption of data and AI tools, delivers localized training, advises campaign teams, tracks governance and adoption, and pilots market-specific AI solutions. Coordinates regional and global stakeholders while translating complex technical capabilities into practical workflows, playbooks, reporting, and measurable campaign improvements across China, Taiwan, and broader APAC markets.

Support AI prototypes, AI/BI solutions, AI agents, GenAI workflows, dataset preparation, model evaluation, dashboards, and reporting. Assist with testing, issue investigation, documentation, deployment, monitoring, security, compliance, and governance. Collaborate with technical teams and business users while learning new AI and BI tools during a six-month internship.

Develop and test AI models, analyze and cleanse datasets, build prototypes, research machine learning technologies, and document solutions. Collaborate with supply chain, IT, sales operations, marketing, and finance teams to identify AI opportunities and optimize processes using Lean Six Sigma. Present technical concepts and solutions to stakeholders while gaining hands-on experience through mentorship and professional development.

Develop and optimize computer vision and machine learning models for facial image feature extraction. Convert extracted features into useful formats and collaborate with research engineers to build proof-of-concept systems and demonstrators. The role requires Python and machine learning libraries, experience with PyTorch and OpenCV, knowledge of computer vision and image processing, version control skills, and the ability to work independently.

Develops and executes fraud strategies across APAC markets, monitoring transaction and merchant fraud, investigating emerging threats, defining metrics, and designing scalable controls. Partners with Product, Engineering, Data Science, Operations, and Partnerships to mitigate losses while balancing user experience, conversion, cost, and growth. Assesses launch risks, improves workflows, communicates findings to stakeholders, and drives cross-functional execution using data and analytical judgment.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Supports corporate sales and structuring activities across global markets through pricing assistance, market analysis, business reporting, process improvement, compliance support, client events, communications, and strategic projects. The intern works with sales, structuring, and business management teams while developing financial markets, data analytics, stakeholder coordination, and project management skills.

Designs and supports reliability engineering solutions involving monitoring, observability, automation, AI-assisted operations, and cloud technologies. The role improves service reliability, reduces operational toil, coordinates integration testing, analyzes incidents and telemetry, develops self-service capabilities, and supports major production incidents. It also applies ITIL service management practices, facilitates problem management, recommends resilience improvements, and provides technical guidance to less experienced engineers.

The AI Product Owner will drive AI integration into the Razer Gold Digital Wallet, optimizing operations and enhancing user experience through innovative features and data-driven insights across teams.

Lead high-impact data science initiatives from problem definition through production deployment and iteration. Design and deploy machine learning, AI, and analytical solutions, translate ambiguous business problems into measurable objectives, and partner with senior stakeholders on strategy and roadmaps. The role also provides technical leadership and mentorship across the data science community. Strong Python, SQL, statistics, machine learning, production modeling, and large-scale data experience are required.

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.

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.

Build, deploy, maintain, and monitor scalable machine learning systems, infrastructure, pipelines, and applications. Collaborate with data scientists, engineers, clients, and stakeholders to deliver ML solutions, lead functional workstreams, explore emerging tools, address technical challenges, and mentor team members. Apply MLOps, CI/CD, cloud and on-premises infrastructure, model training, deployment, monitoring, and evaluation practices while contributing to the organization’s machine learning strategy.