Top Machine Learning Jobs in Singapore

Lead design and implementation of an end-to-end AML risk management framework for overseas businesses: build KYB/CDD/EDD processes, risk-rating models, real-time suspicious transaction monitoring, STR/SAR reporting, and dynamic lifecycle risk controls while ensuring compliance with international AML regulations across jurisdictions.

The role involves developing AI-driven banking solutions, writing efficient code, integrating systems, and ensuring software quality through testing and reviews.

The role involves conducting research to identify predictive financial signals and contributing to quantitative models for market predictions.

Design, build, and productionize GenAI and ML solutions for semiconductor engineering: large-scale data pipelines, LLM/RAG and agentic workflows, model training/inference, analytics, deployment, monitoring, and cross-functional collaboration to improve quality, cost, and engineering productivity.

Lead development and delivery of AI/ML analytics for integrated surveillance across AML, sanctions, fraud and scams. Define data science strategy, build detection models and reusable risk features, ensure model governance and explainability, partner with data engineering and compliance stakeholders, drive production deployment and adoption, and mentor analytics teams to address emerging financial crime risks.

Support the development team by testing and documenting simple AI features, preparing prompts and datasets, performing basic SQL data checks, troubleshooting applications, and learning development workflows and tools under guidance.

The Senior GenAI Scientist develops AI models to enhance marketing initiatives by collaborating with various teams, optimizing systems, and deploying solutions.

Design, build, and maintain cloud-native applications using traditional code and OutSystems low-code. Integrate AI/ML models and cloud AI services, develop microservices, containers, and serverless functions, implement CI/CD and DevOps practices, create responsive web and mobile apps, integrate external systems/APIs, participate in agile ceremonies, conduct code reviews and testing, and produce technical documentation and proofs-of-concept.

Lead transformation of actuarial processes through automation, analytics, and AI. Identify use cases, define requirements, support solutioning, testing and UAT. Manage actuarial data and build reusable analytics pipelines for persistency and claims, improving data quality, lineage and reporting. Support AI pilot delivery and scaling for actuarial reporting and MI.

Lead modernization of actuarial reporting through automation, process redesign, enhanced analytics and AI. Manage PACS/FFP+ projects, define actuarial AI roadmap, deliver pilots and scaled solutions, and coordinate cross-functional stakeholders to improve reporting efficiency, controls, and actionable insights.

Design, build and test prototypes using emerging technologies (AI/GenAI, autonomous agents, multimodal AI, quantum). Collaborate with product, design, and engineering teams to iterate, showcase proofs-of-concept, and drive adoption across the bank while researching frontier tech and enabling rapid experimentation.

As a Research Engineer, you'll work on building and improving a scalable search engine, focusing on crawling, parsing, ML performance, and retrieval algorithms.

Serve as Anthropic's founding Commercial Counsel in Singapore, advising APAC sales on complex technology transactions, negotiating agreements, ensuring regulatory compliance for AI deployment across APAC, and designing scalable contract frameworks and playbooks.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

Work with the CTO and leadership to design and implement AI productivity solutions; research and prototype emerging AI technologies; collaborate across teams and continents; translate AI theory into scalable business applications and communicate technical ideas to non-technical stakeholders.

The intern will design safe processes, analyze behavior for anomalies, track fraudulent accounts, and maintain machine learning models to enhance account safety.

Design, build, and maintain production-quality cloud-based systems and data pipelines for autonomous vehicle monitoring and safety assurance. Develop analytics, dashboards, alerts, and integrations, support production incidents, and apply AI/ML and data visualization to improve deployment safety and scale.

Lead AI discovery engagements across APAC, facilitate workshops, assess and prioritise AI use cases, build adoption roadmaps, coordinate cross-functional teams, coach customers on agile/DevOps practices, and position Red Hat products to drive measurable AI adoption and outcomes.

The Senior Machine Learning Engineer will lead the deployment of ML models, mentor engineers, and influence product decisions, while collaborating directly with customers on-site to solve engineering challenges across various industries.

The Principal Machine Learning Engineer will lead efforts in deploying ML models, architecting data systems, mentoring teams, and driving project scoping with a strong focus on customer collaboration.

The Research Associate will develop methods for visual reasoning and causal reasoning in a Vision-Language project, focusing on deep learning techniques.

The Research Scientist will conduct innovative research in cloud computing and distributed systems, developing algorithms to optimize performance and efficiency through machine learning techniques.

The Research Engineer will conduct R&D on vector search systems and algorithms, design experimental frameworks, and collaborate on research publications.

Conduct innovative research in advanced machine learning focusing on recommendation systems and graph neural networks, designing novel models, and analyzing large-scale data.