Top Machine Learning Jobs in George Town

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.

Analyzes quality and EHS data from multiple sources, develops Power BI dashboards and reports, identifies trends and anomalies, applies statistical and predictive modeling techniques, integrates TQM systems with analytics platforms, conducts risk assessments, and presents actionable recommendations to stakeholders and executives.

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.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

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.

Leads global supply chain optimization by designing scalable processes, standards, training, metrics, and technology capabilities. Oversees procurement and ERP roadmaps, Oracle Fusion governance, AI and analytics adoption, MRP policies, standard costs, supplier performance, and continuous improvement. Drives transformation initiatives, change management, shared services, executive reporting, and M&A integration across divisions and geographies.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

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.

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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Designs and develops content governance platforms, anti-abuse tools, rule engines, and scalable governance pipelines. Integrates recommendation systems, implements content takedown policies, and builds capabilities for content review, risk detection, policy enforcement, and effectiveness analysis. The role addresses spam, fraud, traffic diversion, low-quality content, and black- or gray-market activity while collaborating with product, operations, algorithm, and business teams.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Support APAC Claims Operations by producing and automating reports, reconciling multi-system data, resolving quality issues, and developing scalable dashboards. Build SQL queries, Python automation workflows, data validation processes, and documentation. Deliver ad hoc analysis and leadership insights while piloting AI applications such as claims classification, anomaly detection, document summarization, and natural-language querying. Partner across markets to improve reporting efficiency, data quality, and operational decision-making.

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.