Top Machine Learning Jobs in Singapore

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.

Develop advanced AI, machine learning, forecasting, optimization, control, and predictive analytics solutions for building automation and energy management. Build mathematical models, simulations, digital twins, and scalable microservices; analyze industrial IoT data; and deploy solutions to cloud and edge environments. Collaborate with architects and business teams to integrate data-driven capabilities into products, identify growth opportunities, communicate customer value, and promote data science best practices. Mentor engineers and support globally distributed teams.

Develops advanced AI, machine learning, forecasting, optimization, control algorithms, mathematical models, simulations, digital twins, and predictive analytics for building automation and energy management. Analyzes industrial IoT data, deploys scalable machine learning solutions to cloud and edge environments, integrates algorithms into customer-facing platforms, mentors engineers, and communicates data-driven opportunities and outcomes to business teams.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Leads finance transformation by owning core financial processes, modernizing ERP and planning systems, deploying AI and automation, partnering with engineering and data teams, redesigning workflows, establishing controls, and driving organizational adoption. The role requires senior finance leadership, strong systems expertise, hands-on AI transformation experience, process governance, and the ability to translate between finance and technology.

Own the end-to-end product direction and quality of an AI-native email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI workflows.

Own the end-to-end definition, evaluation, and quality of AI-powered email experiences. Translate model capabilities and technical constraints into product requirements, system decisions, priorities, and measurable outcomes. Partner with ML, backend, and client engineers to improve reliability, safety, latency, cost, usability, and trust. Identify failure modes, establish quality bars, drive experimentation, and guide execution in a fast-moving zero-to-one environment.

Own the strategy, requirements, quality, and execution of an AI-native email application. Partner closely with ML, backend, and client engineers to define model and system behavior, evaluation frameworks, product trade-offs, and reliable user experiences. Drive prioritization, experimentation, failure-mode mitigation, and continuous iteration while balancing quality, latency, cost, safety, reliability, and user trust in a fast-moving zero-to-one environment.

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.

Owns the end-to-end product strategy and execution for AI-powered systems, translating model capabilities, technical constraints, and user needs into measurable requirements and product decisions. Partners closely with ML, backend, and client engineering teams on system design, evaluation, experimentation, reliability, safety, and user experience. Establishes quality standards, identifies failure modes, prioritizes roadmaps, and drives rapid iteration for predictable, useful, and trusted AI products.

Build and deploy human behavior simulation models using survey and interview data, LLMs, and machine learning. Rapidly prototype modeling, prompting, fine-tuning, and evaluation approaches while advancing research into behavioral fidelity, bias, failure modes, and extrapolation. Work directly with enterprise clients in Singapore to deliver tailored solutions, then hire and lead the team scaling the Singapore practice.

Develop and validate AI-driven algorithms for advanced optical metrology and semiconductor analytics. Responsibilities include researching methodologies, analyzing technical data, prototyping and fine-tuning machine learning models, benchmarking performance, defining product features, writing software requirements, and collaborating with application, product, marketing, and software engineering teams to deploy reliable production solutions.

Leads the bank’s AI and data assurance strategy, overseeing model validation, AI risk mitigation, data quality, privacy, governance, controls, monitoring, regulatory reporting, stakeholder training, and team leadership. Ensures AI and data initiatives are ethical, compliant, secure, reliable, and aligned with financial-services regulations and internal policies.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Develop, fine-tune, deploy, and optimize machine learning models and AI-powered applications for enterprise customers. Build and present proofs of concept, integrate new models, improve the internal ML platform, resolve bugs, and enable partner solutions. Collaborate with sales and solutions teams to deliver scalable AI deployments and successful customer outcomes.

Conduct applied scientific machine learning research focused on PINNs, physics-constrained loss functions, digital twins, uncertainty quantification, Bayesian experimental design, or causal reliability modeling. Develop synthetic-data methods, validate models against physical ground truth, guide laboratory experiments, and deliver production-ready research prototypes with complete documentation. Required work includes deep PyTorch or JAX implementation, research output, and collaboration with storage-domain experts. Preferred experience includes diffusion models, GNNs, Neural ODEs, foundation model fine-tuning, reinforcement learning, and materials or semiconductor domains.

Own the architecture, deployment, and operation of machine learning systems for precision product development, including computer vision, anomaly detection, physics-informed models, surrogate modeling, and active learning. Lead MLOps platform development using MLflow, Docker, AWS EKS, observability tools, and CI/CD. Provide technical direction, conduct design and code reviews, mentor engineers, and partner with scientists and engineering teams to deploy production ML solutions.

Build and evaluate machine learning models for precision product development, including CNN-based image classification and defect detection. Validate training data, track experiments with MLflow, produce evaluation reports, package models with Docker, contribute to CI/CD, and support inference and deployment validation. The role also provides exposure to physics-informed AI, Bayesian methods, surrogate modeling, active learning, and scientific ML systems.

Own machine learning systems for product-development inspection, material modeling, anomaly detection, and active learning. Build and maintain deep learning models, surrogate-model pipelines, data-to-model interfaces, and MLOps workflows. Responsibilities include model evaluation, uncertainty analysis, monitoring, data validation, CI/CD contributions, documentation, code reviews, and junior mentorship. The role requires hands-on ownership from training through deployment using Python, PyTorch, MLflow, Docker, and Git.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Architects and scales manufacturing data ecosystems, advanced analytics, machine learning models, agentic workflows, and intelligent automation pipelines. Develops production-grade solutions for yield optimization, predictive maintenance, and operational efficiency using Python, R, SQL, and modern data platforms. Partners with manufacturing, supply chain, engineering, and leadership teams to translate complex data into strategic decisions. Leads multidisciplinary teams and establishes best practices for AI adoption and data products.

Own the strategy, requirements, evaluation, and quality of AI agent products. Partner with ML, backend, mobile, and engineering teams to balance model quality, latency, cost, reliability, and user experience. Define evaluation frameworks, guide technical trade-offs, drive execution, and ensure AI systems are predictable, trustworthy, and effective in real-world workflows.

Own the strategy, requirements, evaluation, and execution of reliable AI agent systems. Partner with ML, backend, and mobile engineers to translate model capabilities into user-facing products, balancing quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, oversee product quality, and establish feedback loops that improve correctness, predictability, safety, and user trust.