Top Machine Learning Jobs in Malaysia

Leads NAND component reliability engineering for new memory products, including reliability test strategy, stress plans, execution, data analysis, failure analysis, qualification, and cross-functional issue resolution. Manages and mentors reliability engineers while partnering with product, design, R&D, fabrication, systems integration, and quality teams. The role also involves applying SQL, machine learning, generative AI, and advanced analytics to improve reliability workflows and productivity.

Manages procurement for capital equipment, spares, and services, coordinating procure-to-pay processes, budgets, delivery, invoices, supplier relationships, qualification, negotiations, contracts, and performance management. Drives supplier savings, localization, compliance, and supply-chain resilience. Partners cross-functionally with engineering, finance, and category teams while leading AI-enabled automation and continuous-improvement initiatives. Requires at least seven years of engineering, procurement, or supply-chain experience; semiconductor equipment experience is preferred.

Manage procurement for capital equipment, spares, and services; coordinate procure-to-pay activities, delivery, budgeting, and invoice reconciliation; oversee supplier relationships, qualification, performance, negotiations, contracts, and cost savings. Drive supply chain localization and resilience while supporting AI-enabled process improvement and operational excellence across global, cross-functional teams.

Analyzes, audits, designs, and optimizes telecom networks across RAN, Transport, Core, OM, and Services Networks. The role supports pre-sales, customer requirements gathering, service scoping, cost estimation, proposal development, and service delivery. It requires network engineering, automation, machine learning, data analytics, customer consulting, stakeholder management, and financial awareness.

Supports the Test Engineering team in developing a systematic qualification and data review process for relocated SMT6 semiconductor testers. Responsibilities include defining setup datasets, capturing configuration and qualification evidence, creating review workflows, tracking issues, developing reporting dashboards, and piloting AI-assisted checks for missing data, anomalies, trends, and failure signatures. The intern will help standardize tester setup documentation, qualification readiness, release decisions, and reusable engineering playbooks.

Develop an automated PPJ performance tracking solution for Site Test Equipment Engineering. Extract and consolidate database data, generate Power BI trend charts, map equipment IDs and alarm codes to faulty parts, and enhance dashboard filtering by test platform, DID, equipment, and alarm category. Prepare documentation and user guidance for adoption and maintenance. Potential stretch work includes scheduled refreshes, abnormal-performance alerts, and root-cause analysis views.

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.

Leads strategic test planning and develops scalable automation frameworks for embedded, cloud, web, mobile, and API platforms. Oversees performance, security, UI, integration, and acceptance testing; integrates automated tests into CI/CD pipelines; evaluates testing tools and methodologies; applies AI to automation; and mentors engineers. The role requires cross-functional leadership, advanced problem solving, and end-to-end testing expertise for Motorola Solutions products.

Leads end-to-end device and system testing for embedded video products. Develops and maintains Python test automation, CI/CD pipelines, test tools, infrastructure, and documentation. Analyzes requirements, improves coverage, investigates false failures, manages defects, and validates issues with engineering teams. Supports AI model testing and automation enhancements, performs vehicle drive testing as needed, and provides technical leadership and mentorship.

Analyze complex datasets, develop and optimize statistical and machine learning models, and create AI solutions for business challenges. Clean and validate data, evaluate model performance, and collaborate with engineering, IT, and business stakeholders on deployment and improvement. Communicate analytical findings through reports, visualizations, and presentations while translating technical results into actionable recommendations. The role also requires staying current with data science, machine learning, generative AI, and responsible data practices.

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.

Develop statistical and machine-learning models, analyze complex datasets, design experiments, and deliver data-driven solutions for marketing and customer-data products. Responsibilities span problem definition, data exploration, feature engineering, validation, monitoring, and communicating findings. The role collaborates with Product, Data Engineering, Scrum teams, and business stakeholders, with opportunities to work on embeddings, RAG, prompt engineering, audience taxonomy, spend analytics, and other AI initiatives.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Supports the COO Team with client profile and transaction reviews, control activities, process improvement and automation initiatives, and preparation of reports for senior stakeholders. The role requires analytical ability, attention to detail, communication skills, advanced Excel and VBA knowledge, and an understanding of core AI and machine learning concepts.

Develops, optimizes, qualifies, and improves substrate manufacturing processes to ensure quality, reliability, yield, and process capability. Responsibilities include troubleshooting, root cause analysis, DOE, statistical analysis, process monitoring, new product introduction, materials qualification, FMEA, CAPA, documentation, safety compliance, and collaboration with manufacturing, quality, equipment, suppliers, and external partners. AI and data analytics support predictive monitoring and continuous improvement.

Develop machine learning and advanced analytics solutions for risk management, including predictive models, GenAI, agentic AI, data pipelines, dashboards, and automated reporting. Analyze complex datasets to identify emerging risks, control weaknesses, and non-compliance. Collaborate with stakeholders to deliver scalable analytical solutions, support model deployment and monitoring, and communicate insights to technical and executive audiences.

Lead the global strategy and roadmap for recommendations and personalization across three ecommerce platforms. Partner with data science, analytics, engineering, and regional teams to translate customer intent and machine-learning capabilities into scalable discovery experiences. Own experimentation, relevance, engagement, conversion, and commercial outcomes while balancing shared capabilities with local market needs.

Lead the design, deployment, and architecture of production-ready AI, data science, and analytics solutions for tax administration. Develop Generative AI, RAG, and Agentic AI applications; provide technical guidance to stakeholders; translate data into strategic insights; define long-term AI architectures; and mentor data professionals. The role requires strong machine learning, programming, cloud deployment, full-stack, data engineering, and communication skills.

Develop and deliver data analytics tools, algorithms, and methodologies to translate manufacturing data into insights for factory yield and process engineering. Apply predictive modeling, clustering, anomaly detection, time-series analysis, and ML frameworks to support engineers in semiconductor manufacturing.

Resolves moderately complex technical customer issues across Zebra products, manages support tickets and SLA adherence, collaborates with cross-functional teams, improves support processes, develops training for junior staff, and contributes to technical reviews and product improvements. The role requires knowledge of MDM platforms, SQL, cloud services, Linux, Android, hardware, software, and cloud-based systems, with rotating 24x7 shift coverage.

Lead risk-based internal audit planning and end-to-end engagements for a global cryptocurrency exchange. Evaluate governance, risk, and controls; produce audit reports for management and the Board; monitor remediation; and use SQL, AI, and advanced analytics to improve audit testing and risk assessment. Collaborate with Compliance, Risk, and Finance teams while addressing cross-border regulatory requirements and emerging risks in cryptocurrency, DeFi, and blockchain.

Support the development and optimization of AI and machine learning models for predictive maintenance and asset failure prediction. Process multi-source engineering data for anomaly detection and help deploy Power BI dashboards supported by automated data pipelines that provide live AI model health scores for executive reporting.

Develop and deploy production-ready AI and machine learning solutions for operations, sales, and service use cases. Responsibilities include data preparation, feature engineering, model development, validation, deployment, monitoring, governance, and retraining. The role also involves exploratory analysis, proof-of-concept development, collaboration with data engineers and stakeholders, and communicating technical insights to business audiences.

Drive new-logo acquisition and expansion across Napier AI’s Asia-Pacific client base. Build territory plans, develop senior relationships within financial institutions and regulated organizations, manage complex enterprise sales cycles, deliver proposals and business cases, navigate procurement and legal processes, negotiate commercial terms, and partner with internal teams on upsell, cross-sell, renewals, and customer growth.

Develop and deploy predictive, prescriptive, and machine learning models for supply chain challenges such as demand forecasting and inventory optimization. Build Python, SQL, and data models, apply advanced statistical techniques, validate data quality, and identify trends and anomalies. Partner with supply chain teams to translate business needs into analytics solutions, present insights, and continuously improve deployed models.