Top Machine Learning Jobs in Singapore

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

The role involves developing AI models, translating business problems into data science solutions, and integrating models into a marketing platform for influencer marketing.

Develop AI systems for 3D and video generation, conduct research in computer vision and graphics, collaborate with teams, and apply innovative AI techniques.

The Lead Data Scientist will develop machine learning solutions for fraud detection, manage model creation, deployment, and collaborate to ensure business impact.

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.

Support regional P&C pricing by preparing actuarial analyses, validating data for predictive models (GLM/ML), performing account pricing, and building monitoring infrastructure. Extract and cleanse data, enhance processing efficiency, and collaborate with stakeholders across countries to drive pricing strategy and portfolio performance.

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.

Work with Product, Business, and Engineering to collect, clean, analyze data, build Python and SQL automation, contribute to internal analytics platforms, assist cloud deployments, and document technical solutions to support decision-making.

Lead digital, data, and AI adoption by partnering with business leaders, designing enablement and change programs, supporting pilots, measuring adoption KPIs, and advising stakeholders to scale user-centric analytics and AI solutions.

Lead adoption and scaling of AI across supply chain: define roadmap, prioritize use cases, manage solution lifecycle from pilots to production, establish AI governance and playbooks, improve data readiness, drive stakeholder engagement, build community of practice, and measure business value.

Lead implementation and enhancement of AML/CFT transaction monitoring systems across the Group. Act as SME for TM solutions, drive threshold tuning, scenario calibration, analytics, AI/ML and RPA adoption, manage change requests, projects, training, and vendor/technology collaboration to meet regulatory and operational objectives.

Lead credit risk management for customer accounts, enhancing profitable growth through innovative strategies and compliance with regulations. Oversee talent development and integrate advanced data capabilities.

Lead and mentor a logistics analytics team to deliver insights on fleet performance, delivery efficiency, and cost optimization. Shape analytics roadmap, design performance metrics and AI data products, apply decision science and operations research, manage end-to-end analytics execution, and present executive-ready recommendations to drive measurable improvements.

Design, develop and roll out geospatial AI-centric MVPs and digital products. Work full-stack on frontend and backend, implement and fine-tune ML/GenAI/LLM models, use cloud and DevSecOps practices, collaborate with stakeholders in cross-functional teams.

Lead engineering projects, automation and digitization, new product qualification, and machine/process reliability. Manage budgets, timelines, root-cause troubleshooting, maintenance strategy, and integration of advanced manufacturing technologies. Mentor and develop engineering team, ensure safety and compliance, and coordinate with regional/global stakeholders to drive productivity and operational excellence.

Enterprise sales role selling FICO's optimization and decisioning platform into financial services. Build C-level relationships, develop pipelines and joint business cases, lead complex deal structuring and negotiation, coordinate pre-sales and professional services, manage forecasts and territory targets, and travel to client meetings (25-50%).

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

The Product Applications Specialist supports sales in microscopy software, provides customer training, develops solutions, and resolves technical issues in various scientific fields.

Two-year Talent Accelerator for Senior-level scientists to integrate biology, data, and AI in translational drug discovery. Work in multidisciplinary teams to develop translational models, spatial omics and imaging assays, AI/ML pipelines, and multimodal data integration to identify biomarkers, inform target decisions, and communicate findings to stakeholders.

Lead a data insights team to deliver analytics, AI and automation solutions that drive measurable business outcomes. Partner with stakeholders to translate problems into analytical initiatives, manage end-to-end analytics lifecycle, operationalize AI-enabled capabilities, establish KPIs and reporting, and mentor data talent to build organizational analytics maturity.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.