Top Machine Learning Jobs in Singapore

Lead global Wealth Management Channels technology, driving architecture, product delivery, and engineering culture for multi-channel digital products. Manage large budgets and teams, build secure, scalable systems (iOS/Android/Web), implement DevOps/automation, and ensure operational excellence and regulatory compliance.

Develop, train, fine-tune, evaluate, and deploy AI/ML models and LLM-powered data products. Build end-to-end model pipelines, feature engineering, monitoring, and production deployment. Apply generative AI for summarization, classification, knowledge extraction and semantic search. Collaborate with product, engineering, and business teams and integrate third-party tools and open-source models.

Leads regional Payment Operations transformation, service excellence, and AI-enabled digital initiatives. Drives predictive analytics, automation, workflow digitization, and scalable technology solutions using Python, SQL, Power BI, Power Automate, APIs, and Generative AI. Oversees projects from business case through implementation, benefits realization, governance, operational readiness, and regional rollout. Partners across Operations, Technology, Product, Risk, Compliance, vendors, payment networks, and country teams to improve productivity, resilience, controls, quality, and client outcomes.

Lead end-to-end architecture for enterprise risk and compliance platforms covering credit, market, liquidity, operational risk, AML, KYC, fraud, sanctions, and screening. Design scalable, secure, explainable, audit-ready solutions aligned with Basel, IFRS9, MAS, and FATF requirements. Produce architecture patterns and design artifacts, govern deviations and technical debt, and collaborate with trading, risk, operations, finance, engineering, data science, and platform teams.

6-month internship to design and implement AI-enabled software for telecom Digital Twin platforms. Responsibilities include Python backend and API development, telemetry ingestion, ML model integration, web dashboard visualization, performance optimization, documentation, and final demonstrations to leadership.

Develop and evaluate machine learning models for recommendation, ranking, content understanding, and user behavior modeling. Own an end-to-end internship project involving problem framing, feature engineering, training, evaluation, experimentation, and potentially production deployment. Analyze results, collaborate with engineering and product teams, and contribute to discussions on model architecture, system design, and performance trade-offs.

As an AI Product Manager, you will define product roadmaps, run engineering sprints, conduct user testing, and ensure team alignment while translating user pain points into product priorities.

Develop and validate advanced algorithms, physics-based models, and ML/data-fusion methods for CD-SAXS and OCD metrology. Collaborate with cross-functional teams to prototype, test, and transition research into production-ready solutions; publish papers and file patents. Improve measurement accuracy, robustness, and computational efficiency for semiconductor metrology.

The role involves managing AI and data science projects, engaging stakeholders, and delivering data-driven solutions while leading a team and guiding junior members.

Design, implement and maintain simulation and testing environments for drone communication protocols. Build intelligent fuzzing and security evaluation frameworks, traffic-analysis and anomaly-detection methods, and automated validation tools. Conduct adversarial experiments, analyze results, develop LLM-based fuzzing agents, and prepare technical documentation, reports and publications to meet project milestones.

Lead technical pre-sales for computer vision and AI, design testing solutions, scope customer requirements, and maintain client relationships.

The Senior Data Scientist will identify fraud patterns, develop machine learning models, collaborate with technical teams, and provide analytical insights to mitigate risks in crypto transactions.

As Senior Product Manager, you'll lead the development of risk products, manage strategies, and enhance risk models to prevent fraud while ensuring customer satisfaction.

As a Quantitative Research Analyst, you will develop models, conduct complex data analysis, and work with stakeholders to optimize investment strategies.

The Product Manager will lead product development, define vision and roadmap, manage engineering activities, and collaborate with various stakeholders.

The Business Development Manager at HPE drives growth through strategic partnerships with NVIDIA, focusing on expanding market presence and supporting sales enablement for AI solutions.

Lead end-to-end data science initiatives for commodity trading, focusing on palm production, weather modeling, commodity flows, tanker analytics, and market insights. Build scalable databases, dashboards, reports, alerts, and decision-support tools using large datasets. Partner with traders, researchers, technology teams, and leadership to drive projects from ideation through adoption. Mentor junior data scientists, establish best practices, and translate complex analysis into actionable commercial recommendations.

Leads APAC strategic alliances and partner sales for identity verification and fraud prevention solutions. Develops partner strategy, recruits and manages resellers, ISVs, and system integrators, drives joint go-to-market initiatives, resolves channel conflicts, aligns direct and partner sales teams, and accelerates indirect revenue. Builds executive partner relationships, manages sales pipelines, coordinates cross-functional engagement, negotiates deals, and delivers quota attainment. The role requires occasional travel and experience selling into fintech, crypto, payments, and e-wallet markets.

Own the end-to-end product strategy and execution for AI-native applications. Define AI system requirements, evaluate model behavior, make trade-offs across quality, latency, cost, reliability, and user experience, and partner closely with ML, backend, mobile, and engineering teams. Establish evaluation frameworks, prioritize roadmaps, drive rapid and safe delivery, and ensure product correctness, predictability, and user trust.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Develop and deploy end-to-end AI solutions for industry partners, from scoping and feasibility assessment through maintenance. Mentor AI apprentices through technical coaching, project delivery, and recruitment activities. Apply machine learning, generative AI, LLMs, software engineering, MLOps, and cloud technologies. Collaborate with external organizations to define requirements and deliverables, while supporting AI Singapore’s outreach, talent development, and ecosystem-building initiatives.

Design, validate, and deploy AI algorithms for digital health projects. Lead technical innovation, support data platform development and retrospective analysis, collaborate with clinicians and biostatisticians, publish findings, prepare technical reports, and mentor junior AI researchers.

Nine- to ten-week summer internship for undergraduate or graduate students in engineering. Participants receive orientation and training, work on real engineering projects across scalable software, low-latency infrastructure, cybersecurity, mobile, and machine learning, and collaborate with teams to solve technical problems.

Design, build, and operate backend services and data pipelines to ingest, process, enrich, and deliver large scientific, media, and geospatial datasets. Implement REST APIs, event-driven and streaming systems, and Kubernetes-based production infrastructure to support machine learning and agentic AI applications. Collaborate with scientists and ML engineers to translate requirements into scalable, reliable solutions.

Lead the risk product suite roadmap and execution for merchant onboarding, fraud prevention, and credit risk. Manage a risk engineering and data science squad, build real-time risk engines and ML models, optimize KYC/KYB onboarding flows, and develop lending/settlement risk models to minimize financial loss while preserving merchant and buyer experience.