Top Machine Learning Jobs in Singapore

Conduct quantitative research and statistical analysis on crypto markets to develop, backtest, and implement automated trading strategies. Work with large and unconventional data sets to build valuation models, refine algorithms, and deploy signals in live trading to drive P&L growth.

Supports technology and market research, emerging technology analysis, new product concepts, requirements gathering, feasibility assessments, user journeys, prototypes, MVPs, and AI-enabled hardware evaluations. The intern researches AI, machine learning, robotics, IoT, sensors, and edge devices; prepares market findings and presentations; and documents testing results, performance, and commercial use cases.

The Senior Solution Consultant supports complex enterprise sales by conducting technical discovery, workshops, demos, RFx responses, solution design, and architecture documentation. The role maps customer business and technical needs to Genesys solutions, develops reusable integrations and infrastructure solutions, and presents business value to executive stakeholders. Responsibilities include leading presales teams, creating sizing calculations and diagrams, advising on CCaaS and AI technologies, and collaborating with sales, partners, architects, engineers, and services teams.

Owns end-to-end machine learning systems across data, training, evaluation, inference, deployment, and monitoring. Builds large-model training and fine-tuning pipelines, evaluation frameworks, high-performance GPU inference systems, and reliable production infrastructure. Partners with research and application engineering to convert model capabilities into measurable product improvements. Provides hands-on technical leadership, makes pragmatic trade-offs, and ensures ML systems are scalable, efficient, safe, reliable, and production-ready.

Lead execution of end-to-end ML systems: data pipelines, training workflows, evaluation, and inference. Translate research into production by fine-tuning large models (LoRA/QLoRA/SFT/DPO/distillation), designing scalable GPU inference, ensuring memory/latency optimization, building evaluation for performance/robustness/safety, and integrating ML into backend and client products while operating under real-world constraints.

Lead production-grade ML systems: build and operate end-to-end ML pipelines, fine-tune large models (LoRA/QLoRA/SFT/DPO/distillation), design scalable inference, ensure GPU optimization, implement evaluation for performance/robustness/safety, and collaborate with product and application teams to ship reliable, low-latency AI features.

Own end-to-end ML systems: data, training, evaluation, inference, and deployment for production-grade LLMs. Build scalable training and inference pipelines, optimize GPU performance and memory, maintain evaluation and safety tooling, and collaborate with application engineers to integrate models into products while balancing latency, cost, and reliability.

Designs and maintains Databricks-based ETL/ELT pipelines on AWS, develops optimized data models for analytics and BI, and supports production GenAI integrations. The role collaborates with data scientists, AI engineers, and business teams to operationalize models, manage data dependencies, implement monitoring and quality standards, and ensure secure, compliant data workflows.

Design and implement AI-powered productivity solutions, research and evaluate emerging AI technologies, and translate theoretical AI into scalable business applications. Collaborate with executives, business leaders, and technical teams globally while communicating complex technical concepts to nontechnical stakeholders.

Conduct research on AI-based process monitoring for laser-based metal additive manufacturing. Responsibilities include collecting thermal data, developing data-efficient AI models, detecting anomalies and material defects, implementing real-time layer-wise monitoring, optimizing process parameters, supporting energy-system collaborations, maintaining laboratory resources, documenting findings, mentoring students, and assisting with research proposals and publications.

Own an AI finance product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, launch, and regulatory readiness. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Apply AI with rigorous evaluation, safeguards, human oversight, and reliability controls. Coordinate engineering, design, compliance, operations, treasury, and finance while managing financial rails, third-party dependencies, and trade-offs across correctness, speed, cost, regulation, and user experience.

Own an AI investing product vertical end to end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reliability, security, and scalability. Embed KYC, transaction monitoring, privacy, licensing, and other regulatory requirements. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Conduct numerical modelling and optimal control strategy development for building energy, energy storage, and fire systems. Collect, standardize, and integrate experimental and simulation data; apply AI and machine learning to accelerate simulations; validate models and algorithms; publish research papers and pursue patents. The role also requires progress reporting, independent solution development, and collaboration under strict timelines.

Conduct research and experiments for game AI bots using reinforcement learning and generative AI. Support scalable AI services, optimize deployed models, evaluate performance, and troubleshoot bugs. Explore new approaches to improve model effectiveness while collaborating with the data and AI team.

Conduct research on earthquake hazards, fault geometry, and rheology; generate rapid disaster maps using remote sensing and space geodesy; improve InSAR deformation and coherence time-series analysis with machine learning; analyze geospatial and geodetic datasets; and collaborate with multidisciplinary teams to translate findings into operational applications and publications.

Develop, train, evaluate, optimize, and deploy machine learning and deep learning algorithms for intelligent surveillance products. Responsibilities include data preprocessing, feature engineering, model research, target detection, tracking, recognition, model compression, platform optimization, documentation, and collaboration on product integration. The role requires staying current with AI advances and independently solving complex algorithmic problems.

Develop an AI-enhanced Power BI dashboard integrating supplier, subtier, forecast, and capacity data. Build predictive models to identify supply chain delivery gaps, single-point-of-failure risks, capacity constraints, and geographical or sourcing dependencies. Analyze demand trends and create early-warning indicators and scenario analyses that support supplier account management, proactive risk mitigation, and resilient supply chain planning.

A 12-month graduate programme offering technology and AI/data science tracks. Associates may work in application development, DevOps, cybersecurity, data engineering, AI/ML, infrastructure, cloud, or advanced analytics. Responsibilities may include building GenAI and agentic AI solutions, developing ML models, applying MLOps, governing responsible AI, and collaborating with technical and business teams.

Lead operational excellence for AI applications in a banking environment by managing lifecycle, evaluating performance, and ensuring compliance. Collaborate with teams to enhance agent-driven solutions and optimize prompt tuning for generative models.

Lead enterprise data product and Data-as-a-Service strategy, delivery, operationalization, governance, and adoption within a regulated banking environment. Build reusable, governed data products supporting analytics, AI, reporting, and business decision-making. Establish operating models, metadata and lineage standards, quality controls, self-service consumption channels, and Centre of Excellence practices. Partner with senior stakeholders, engineering, technology, governance, risk, and business teams to prioritize use cases, manage delivery, enable AI context, and track measurable value.

Lead regional AFC/AML analytics capability: design, build, validate and deploy AML/fraud detection models; guide country analytics teams; ensure model governance, data suitability, and production readiness across stakeholders and regions.

The Responsible AI Specialist will develop AI governance standards, conduct risk assessments, engage stakeholders, and promote ethical AI practices across UOB.

Conduct financial crime investigations using AI, network-link analysis, OSINT, transaction reviews, and customer relationship data. Identify money laundering, fraud, sanctions evasion, corruption, mule activity, emerging threats, and criminal networks. Develop investigative methodologies and intelligence products, prepare reports and presentations, and collaborate with compliance, business, and control-function stakeholders. Apply AML, CTF, sanctions, and financial crime risk expertise within banking and wealth management.

Own analytical quality for a global manufacturing Control Tower, translating operational data into KPIs, forecasts, scenarios, early-warning signals, performance narratives, and recommendations. Frame complex decisions, apply statistics, forecasting, machine learning, and AI, validate models, monitor performance and data quality, and lead analytical workstreams with cross-functional stakeholders. Build prototypes using Python, SQL, R, BI tools, and cloud technologies while communicating insights and limitations to senior leaders.

Lead the engineering strategy for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build production APIs, make architectural trade-offs, establish observability and testing foundations, and partner with ML leadership. Lead a small senior engineering team through technical reviews, mentorship, and hands-on development while improving system reliability, latency, scalability, and user experience.