Top Machine Learning Jobs in Singapore

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Lead customer implementations and technical projects for BioCatch anti-fraud solutions across web and mobile applications. Integrate cloud solutions using APIs, SDKs, and third-party technologies; onboard customers and partners; resolve integration challenges; manage timelines and multiple projects; create technical documentation; and conduct workshops and training. The role requires strong development experience, customer-facing communication, project leadership, fluent English, and up to 5% international travel.

Drive customer acquisition and growth across the APJ region by selling Nebius’ AI and cloud platform. Responsibilities include territory planning, enterprise relationship development, consultative solution selling, complex negotiations, deal closing, forecasting, partner coordination, customer engagement, and representing the company at regional events. The role is based remotely in Singapore and requires regional travel.

Drives regional rollout, design, development, deployment, enhancement, and monitoring of GenAI chatbot and voicebot solutions. Builds guided conversations and foundational capabilities, maintains documentation, supports program initiatives, and partners with stakeholders and marketing teams on adoption campaigns. Requires end-to-end AI project delivery, change management, AI/ML and LLM knowledge, analytical ability, teamwork, and strong communication skills.

Design and build production-ready enterprise AI architecture blueprints and open-source Quickstarts, integrate and benchmark model serving and telemetry, ensure secure, compliant deployments, and co-develop integrations with partners.

Lead design and delivery of scalable AI platform solutions across APAC, scope consulting engagements, architect distributed training/inference and production ML systems, mentor regional teams, align customer needs with Red Hat AI Platform, and represent Red Hat in regional/global AI strategy while traveling up to 40% in APAC.

Lead design, implementation, and productionization of enterprise AI solutions on Red Hat AI Platform and OpenShift across APAC. Deliver end-to-end AI workloads (data pipelines, training, LLM serving, inference), drive pilot-to-production, implement responsible AI controls, mentor team members, and produce architecture and operational documentation.

Develop, deploy, and optimize machine learning and AI solutions, including multi-agent systems, RAG applications, LLM integrations, agentic workflows, and production AI models. Evaluate and improve model performance, prompts, and outputs while collaborating with platform, data engineering, data science, and cross-functional teams. Participate in software development, testing, debugging, code reviews, and lifecycle delivery within a DevOps environment.

Develop, deploy, and optimize machine learning and AI solutions, including multi-agent systems, tool-calling capabilities, RAG systems, and LLM-based applications. Evaluate and improve model performance, prompts, agentic workflows, and production reliability. Collaborate with platform teams, data engineers, data scientists, and stakeholders while contributing to testing, debugging, code reviews, and software development best practices.

Develop and deploy production-grade AI and machine learning solutions for industrial manufacturing. Own projects from problem definition through operational integration, conduct data-driven optimization, advance ML and LLM-based methods, and collaborate with external partners. Coach employees on AI, establish standards, and advise stakeholders. The role requires Python, Azure, Kubernetes, infrastructure as code, cloud AI architectures, process simulation, optimization, and experience scaling ML systems.

Lead multiple customer projects on the GRID platform from kickoff through deployment and handover. Partner with engineering and research teams to define technical scope, assess feasibility, manage risks, establish success criteria, and resolve blockers. Serve as the primary customer contact, lead technical discussions, provide project updates, and translate field feedback into product roadmap inputs. The role requires familiarity with machine learning, robotics, software systems, and related development tools.

Drive enterprise and commercial B2B sales for Salesforce Agentforce and Data 360. Identify, qualify, and develop opportunities; conduct technical discovery; present scalable AI and data solutions; create compelling points of view; and align Salesforce capabilities with customer objectives. Collaborate with account teams and cross-functional partners throughout the sales cycle while translating complex AI, cloud data, and platform concepts into measurable business value and exceeding sales targets.

Manages research impact metrics, grant analytics, AI-enabled data automation, dashboards, and reporting. Develops data frameworks, visualizations, and ranking insights while coordinating research communications, publications, events, and faculty achievements. Supports management reports, meetings, and stakeholder engagement across academic, research, finance, communications, and innovation teams.

Leads CCMS and Niagara platform management, HVAC controls optimization, system integration, fault detection analytics, energy performance initiatives, Niagara modernization, DDC migration programs, and engineering projects. Oversees technical standards, governance, cybersecurity compliance, vendor coordination, commissioning, documentation, and team development. Requires strong Niagara, HVAC controls, BACnet, Modbus, project management, analytics, and supervisory experience, with occasional support for critical activities outside normal hours.

Lead the strategy, implementation, enhancement, and transformation of AML/CFT transaction monitoring systems across the Group. Manage regulatory and audit requests, threshold tuning, scenario calibration, alert optimization, change management, vendor and technology collaboration, and overseas stakeholder training. Drive adoption of AI, machine learning, analytics, and robotic process automation to improve financial crime detection and operational efficiency while ensuring regulatory and risk management objectives are met.

Lead the design, development, and delivery of AI and GenAI solutions across business domains. Act as an AI Product Owner, translating business challenges into scalable solutions, managing cross-functional delivery squads, and measuring adoption and business value. Develop machine learning, LLM, RAG, agentic AI, orchestration, and governance capabilities while mentoring technical teams and advancing reusable AI standards and assets.

Own a portfolio of APAC enterprise and mid-market customers, driving retention, adoption, measurable ROI, and expansion. Advise fraud, identity, AML, and compliance teams on risk strategies and workflow optimization. Monitor account health, lead executive business reviews, manage escalations, build success plans, train users, and identify renewal risks and expansion opportunities. Partner closely with Account Management, Product, Support, and Implementation teams while serving as a strategic customer advisor.

Develop automated test equipment solutions for custom semiconductor products, including test architecture, bring-up, characterization, screening, yield analysis, and fault root-cause analysis. Collaborate with design, DFT, operations, reliability, and customer teams to define test strategies and ensure product quality, performance, coverage, and high-volume manufacturing readiness. Use programming and scripting in Linux, with potential AI and machine learning applications for data analysis and test optimization.

Supports supply chain planning and execution by analyzing demand, supply, inventory, backlog, and operational data. Develops reports, dashboards, and performance metrics; assists forecasting, planning, inventory management, new product introduction, and production ramp activities. Collaborates cross-functionally, identifies inefficiencies, contributes to automation and continuous improvement, and presents data-driven recommendations. The role also applies analytical tools, forecasting methods, and emerging technologies to improve planning and operational effectiveness.

Conduct technology scans and proof-of-concepts, preprocess data, engineer features, build machine learning models, and present systems to management and users. Potential projects include statistics, machine learning, data visualization, human language technologies, and graph analytics. Interns work closely with mentors to meet project requirements.

Build and operate end-to-end machine learning systems for misdirected email detection. Responsibilities include data wrangling, feature engineering, model training, evaluation, deployment, monitoring, experimentation, A/B testing, threshold setting, error analysis, and production support. The role partners with product and engineering stakeholders, contributes to technical documentation, participates in on-call rotation, and improves detection efficacy, reliability, scalability, and customer impact.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Design and deploy production-ready agentic AI systems for public-sector fraud detection. Build LLM workflows for emerging risk identification and detection logic, integrate AWS and enterprise data platforms, and develop tested Python applications and APIs. Establish evaluation, monitoring, CI/CD, observability, governance, security, human oversight, and responsible-AI safeguards while collaborating with data scientists, analysts, engineers, and business stakeholders.

Build and operate reliable production ML and AI systems across AWS and Databricks. Responsibilities include developing MLOps components, productionizing models, creating data and feature pipelines, optimizing Spark and SQL workloads, automating CI/CD and retraining, implementing governance and security controls, and establishing monitoring, evaluation, rollback, and incident-response practices. The role also supports generative AI, recommendation, and search workloads while improving reusable engineering patterns and transferring knowledge to internal teams.

Own the AI product vision, roadmap, customer discovery, evaluation metrics, data integrations, product packaging, pricing, and go-to-market. Translate enterprise customer problems into measurable model tasks and turn pilots into repeatable, paid products. Partner with research, retail and FMCG customers, data providers, sales, and leadership while ensuring trustworthy model explanations, privacy compliance, and measurable customer value.