Top Machine Learning Jobs in Ipoh

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 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.

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.

Work with Product, Business, and Engineering to collect, clean, analyze data, build Python and SQL automation, contribute to internal analytics platforms, assist cloud deployments, and document technical solutions to support decision-making.

Lead supplier quality analytics for outsourced foundry, assembly, and test subcontractors. Build ELT pipelines, data validation, automated testing, and interactive dashboards (Spotfire/PowerBI). Use statistical modeling to track, predict, and resolve manufacturing variability, support continuous improvement, and provide weekend rotational support.

Lead a small data analytics engineering team to build supplier data pipelines, ensure data integrity (ELT, validation, governance), develop real-time dashboards (Spotfire/PowerBI), perform statistical modeling to detect manufacturing drifts, and drive continuous improvement with supplier partners in semiconductor manufacturing.

Lead roadmap, planning and delivery of ML/AI products for games. Scope ML efforts, track experiments and metrics, coordinate engineers and game teams, run Agile ceremonies, manage risks and stakeholders, and define evaluation plans for AI features.

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.

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.

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.

The Senior Gen AI Engineer leads the design and strategy for Generative AI use cases, focusing on user research, product management, and collaboration with cross-functional teams to drive AI innovations and solutions.

This role involves leading AI use case discovery and strategy, user research, product concepting, and prioritization of AI initiatives, ensuring alignment with customer needs and business value.

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.

Design and operate cloud-native backend services, large-scale data pipelines, and production ML model deployment. Build APIs, vector/search data storage, CI/CD, observability, and collaborate with ML and frontend engineers to deliver AI-assisted coding and agentic solutions for game development.

Build scalable, cloud-native backend services, large-scale data pipelines, and production ML model deployment on AWS. Own data storage (relational and vector/search), CI/CD, monitoring, and collaborate with ML and frontend engineers to deliver AI-assisted coding and agentic features.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.