Top Machine Learning Jobs in Malaysia

Develop, optimize, and maintain SSD test programs for high-volume manufacturing and NPI. Automate testing with scripts, enhance coverage, debug failures, collaborate with product/equipment teams, monitor metrics, manage test transfers, and mentor junior engineers.

Leads NAND and NAND-based system product engineering, qualification, validation, and failure analysis. Develops automation infrastructure, scripting, AI-enabled workflows, and analytics to accelerate product development and debug. Partners with design, technology, system, and development teams to resolve electrical, physical, manufacturing, and customer-return failures. Requires deep NAND and storage expertise, hardware knowledge, software development proficiency, and at least 10 years of related experience.

Develops and optimizes semiconductor manufacturing process control systems to improve yield, quality, reliability, productivity, and cost. Responsibilities include data analysis, process modeling, SPC/APC/FDC implementation, smart manufacturing integration, process qualification, failure analysis, supplier collaboration, training, and technical oversight. The role applies machine learning, AI, IIoT, and deep learning to create network-wide manufacturing control solutions.

Intern will analyze NAND reliability and qualification data, execute wave tests, collect and analyze logs, support failure analysis, and develop AI-driven automation tools. Responsibilities include presenting engineering insights, creating technical documentation, maintaining source code, and demonstrating productivity improvements. The role may also involve developing a GenAI reliability engineering assistant for data analysis, failure identification, knowledge retrieval, and reporting.

Monitor and improve semiconductor manufacturing equipment performance, uptime, utilization, reliability, yield, and productivity. Troubleshoot failures, lead root cause analysis, implement corrective and preventive actions, and support maintenance programs. Lead equipment installation, commissioning, and qualification. Analyze manufacturing data and apply AI, machine learning, automation, and digital tools to improve operations. Ensure safety and environmental compliance, train technicians, and collaborate across engineering, production, quality, and maintenance teams.

Direct operation, reliability, maintenance, capacity planning, and lifecycle management for site-wide mechanical, electrical, and energy systems in a semiconductor manufacturing environment. Leads root cause analysis, predictive maintenance, digital twin, IIoT, AI, automation, energy optimization, and smart-facility initiatives. Ensures regulatory and ISO compliance, resolves complex operational issues, collaborates cross-functionally, and mentors engineers and technicians.

Develops and manages electrical facility systems, including high- and low-voltage equipment, power distribution, maintenance, relay calibration, and regulatory compliance. Leads smart facilities initiatives using AI, machine learning, predictive maintenance, analytics, digital twins, dashboards, and automated diagnostics to improve reliability, energy efficiency, capacity planning, and operational performance. Collaborates with IT, automation, data science, and global facilities teams while supporting troubleshooting, reporting, and facility expansion.

Maintain and support ELV and critical facility systems, including UPS, access control, networks, solar monitoring, VFDs, PQMS, FMCS, BMS, and SCADA. Lead AI, machine learning, IIoT, predictive maintenance, dashboards, digital twins, intelligent alarming, and smart-facility initiatives. Manage contractors, facility upgrades, CAPEX projects, troubleshooting, root-cause analysis, documentation, and EHS compliance while collaborating with Automation, IT, Cybersecurity, and Facilities teams.

Develops and supports dispatching and full-automation software solutions for semiconductor manufacturing. Responsibilities include solution analysis, design, coding, code reviews, documentation, integration, testing, product assurance, second-line automation support, customer requirement analysis, and global deployment assistance. The role troubleshoots complex factory systems, supports onsite customer rollouts, uses SQL and scripting for data manipulation, and provides informal guidance to new team members.

Develops data-driven insights and machine learning models to support business decisions and revenue or cost goals. Provides analytics reporting, collaborates with business stakeholders to align solutions and standards, contributes to data science strategy, and mentors team members on analytics projects.

Own the strategy and lifecycle of core data platform products, including event tracking, attribution, metadata management, and data warehouse portals. Define data collection, quality, and governance standards; integrate AI capabilities; translate financial and cryptocurrency compliance requirements into data products and reporting solutions; coordinate engineering and business teams; and lead documentation, training, adoption, and continuous platform improvement.

Develops end-to-end fund security and anti-fraud risk strategies across financial products. Analyzes transaction data with Hive SQL and Python, reconstructs attack chains, quantifies losses, evaluates experiments, and deploys real-time rules. Responds to major security incidents, builds risk intelligence libraries, monitors metrics, and improves decision systems through collaboration with algorithm, product, engineering, compliance, and operations teams.

Leads internal audit analytics, automation, fraud surveillance, continuous monitoring, and AI-driven risk detection. Builds SQL models, audit-ready datasets, dashboards, alerts, and automated procedures using data from warehouses, internal systems, and blockchain sources. Partners with audit, IT, and data engineering teams to translate operational risks into analytical solutions, support investigations, quantify findings, and improve audit planning, testing, reporting, and control assessment.

The Data Scientist role involves analyzing data, collaborating with teams on AI/ML projects, and developing models to improve product development and efficiency.

Resolves moderately complex technical customer issues across multiple Zebra products, manages support tickets within SLA targets, collaborates with cross-functional teams, improves support processes, develops training for entry-level staff, and contributes to technical reviews and product improvement insights.

Ensures product and process quality and reliability during manufacturing ramp-up to high-volume production. Responsibilities include developing inspection and testing mechanisms, conducting audits and quality assessments, monitoring performance indicators, managing excursions, performing risk assessments, dispositioning discrepant materials, implementing early detection systems, driving continuous improvement, and addressing customer-reported manufacturing concerns through data-driven engineering solutions.

Lead employer outreach and strategic communications for enterprise programmes: design targeted engagement campaigns, develop content assets, leverage data and analytics (Enterprise360/CRM) to personalise messaging, measure campaign performance, build partnerships with industry bodies, and manage flagship events to increase employer awareness and adoption of workforce development initiatives.

Leads ABB’s Energy Industries sales strategy across Southeast Asia, driving growth, revenue, profitability, market share, and customer satisfaction. Identifies opportunities, manages strategic customer relationships, negotiates key contracts, oversees high-value tenders, and develops execution plans. Directs, coaches, and mentors sales managers and specialists while ensuring accountability and performance. The role requires deep Energy Industries expertise, senior sales leadership experience in automation and digital solutions, advanced technical knowledge, and strong stakeholder, negotiation, and team-management skills.

Own digital product delivery from strategy and requirements through execution and optimization. Responsibilities include prioritizing features, managing roadmaps and backlogs, collaborating with engineering, design, marketing, and stakeholders, analyzing markets and customer feedback, facilitating requirements workshops, and monitoring product metrics. The role focuses on scalable technology products, with preferred experience in fintech, payments, banking, loyalty, e-commerce, or AI-powered platforms.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

As a Data Scientist, you will develop analytical models, manage data infrastructure, translate data into insights, and collaborate on projects across teams.

Drives semiconductor manufacturing yield improvements by identifying yield limiters, performing statistical and failure analyses, debugging logic and memory failures, and correlating test, design, and process data. Develops yield roadmaps, measurement recipes, analytical tools, and modeling systems while supporting advanced process nodes, product introductions, and factory experiments. Collaborates with design, test, process, layout, and manufacturing teams to isolate root causes and improve product manufacturability.

Develop statistical and machine-learning models for product demand forecasting across geographies. Analyze forecast accuracy, bias, and planning deviations; conduct experiments and root-cause analysis; and communicate insights to demand planners and business stakeholders. Productionize analytical models, recommend data strategies, identify data science opportunities, and educate teams on advanced forecasting approaches. The role supports integrated business planning, inventory decisions, and supply planning.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Lead pre-sales, consultative selling, and solution architecture for Experian’s Ascend Platform in Malaysia. Develop tailored SaaS solutions for banks and lenders, manage complex enterprise opportunities, conduct executive workshops, create business cases and technical proposals, and demonstrate capabilities across data, analytics, credit risk modeling, ModelOps, GenAI, and decisioning. Strengthen pre-sales frameworks, support go-to-market execution, assess business processes, and translate regulatory and market needs into product and roadmap insights.