Top Machine Learning Jobs in Malaysia

Design, develop, and deploy machine learning models, build and optimize data pipelines/ETL, perform exploratory analysis, ensure data quality, collaborate with engineering/product/business stakeholders, and deliver analytics-driven insights to support business objectives.

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.

Design, develop, evaluate, and productionize ML models and agentic solutions on Databricks and Microsoft Fabric. Build scalable ETL pipelines, perform feature engineering, run disciplined experiments, monitor models in production, and communicate statistical insights to stakeholders while supporting platform engineering and automation.

Design, build, and deploy production ML models and MLOps pipelines for fraud and AML detection across batch and real-time scoring. Collaborate with engineering and data teams on feature engineering, optimize low-latency model performance, run validation and A/B tests, and mentor junior engineers while aligning to architecture standards.

Design, build, deploy and operate end-to-end ML pipelines on Databricks. Develop production-grade Python and Spark code, implement MLOps practices (MLflow, CI/CD, versioning, monitoring), ensure reproducibility, security and governance, monitor model/data performance, and collaborate with data science, engineering and product teams to deliver scalable, compliant ML services.

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 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.

The Power ML Engineer will tailor existing models for the Japanese power market, integrate market signals, develop features, monitor performance, and work collaboratively on product roadmaps.

As a Lead Data Scientist, you'll manage end-to-end projects, build and deploy AI models, analyze data, and collaborate with teams to enhance user experience.

Contribute to the design and rollout of ML & AI platforms. Write production code and develop scalable ML pipelines and automation.

This role involves designing and implementing machine learning platforms, coding, code reviews, and automating ML pipelines for large-scale workloads.

Lead the Machine Learning Engineering team, architecting and deploying innovative AI platforms, while ensuring scalability and efficiency across Grab's services.