Top Machine Learning Jobs in Kuala Lumpur

Provides technical consulting for AML, sanctions screening, and third-party risk solutions across APAC. Leads RFPs, sales engagements, client implementations, API integrations, data pipelines, analytics, reporting, and risk decisioning initiatives. Advises customers on data architecture, modeling, transformation, and enrichment while serving as a technical SME and customer advocate. Partners with sales, product, and delivery teams, presents at industry events, and monitors regulatory, technology, and competitive developments.

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.

Design, develop, and deploy AI/ML models and infrastructure for semiconductor back-end manufacturing. Collaborate with manufacturing and product teams to preprocess data, run POCs, implement defect detection, yield improvement, predictive maintenance, and integrate models into production via APIs and deployment pipelines. Monitor and improve model performance and support adoption by production teams.

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.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Lead AI transformation across Commercial: identify, prioritize, and deliver AI initiatives (forecasting, pricing, personalization, copilots, automation) that drive revenue, productivity, and data-driven decisions; translate business problems into scalable AI solutions; partner with Technology, Legal, Risk, and Data teams; build AI literacy and lead an execution-focused team.

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.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Lead engineering projects, automation and digitization, new product qualification, and machine/process reliability. Manage budgets, timelines, root-cause troubleshooting, maintenance strategy, and integration of advanced manufacturing technologies. Mentor and develop engineering team, ensure safety and compliance, and coordinate with regional/global stakeholders to drive productivity and operational excellence.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Early-career Performance Engineer in the Binance Accelerator Program. Perform hands-on performance testing and tuning, design AI-powered test strategies using LLMs and ML, build AI-driven anomaly detection and bottleneck analysis tools, research new AI technologies, and improve reliability of large-scale distributed systems.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

Support day-to-day business activities by gathering user requirements, analyzing data, creating documentation, and assisting in AI-related projects for a smoother communication between business and development teams.

Develop, review and monitor credit rating models for the wholesale banking portfolio. Work with large datasets, perform model development, testing, documentation, UAT, and regulatory submissions. Produce performance monitoring reports, engage business experts for insights, and improve model monitoring and automation.

Lead AI and automation product initiatives for Seller Center, partnering with ML, engineering and regional teams to discover, design, validate and launch AI-driven features. Use qualitative and quantitative signals to prioritize work, run model and feature evaluations (A/B tests, shadow/offline tests, cohort analysis), communicate trade-offs to stakeholders, and ensure cross-market alignment and operational scalability.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Lead and expand a major financial-institution account by building C-suite relationships, designing enterprise SaaS solutions, negotiating multi-million-dollar deals, driving account growth, and influencing product strategy. Travel up to 50% and collaborate across sales, product, delivery, and marketing.

Lead planning, coordination, and delivery of clinical document translations. Manage vendors, stakeholders, budgets, timelines, and quality controls. Implement and evaluate AI-enabled translation tools and oversee SME post-editing to ensure regulatory-compliant, high-quality translations for clinical documents.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

Design, build, and deploy scalable ML models and pipelines from research to production. Own feature engineering, model tuning, real-time prediction, MLOps automation, monitoring, and lifecycle management to solve visibility, forecasting, and freight audit problems.

Manage relationships with high-value customers and partners in Malaysia to drive new logo, renewal, and cross-sell revenue for Sophos. Qualify opportunities, build C-level and channel relationships, collaborate with Sales Engineering/Marketing/Channel, maintain Salesforce CRM and forecasts, attend events, and coach teammates while ensuring customer satisfaction.

Maintain and monitor ML models in production, automate and scale data pipelines and model serving, troubleshoot issues, collaborate with cross-functional teams, and promote MLOps best practices and standards.

Manage and operate the Databricks Lakehouse and MLOps platform, ensuring availability, security, governance and cost optimisation. Lead incident/change/service management, define MLOps frameworks and CI/CD for model lifecycle, collaborate with engineering and data science to deploy and monitor models, and manage vendors and platform roadmap.