Senior Machine Learning Engineer (Simulation)
Grab is seeking a Senior Machine Learning Engineer to build and scale simulation platforms, including a digital twin of Grab's marketplace. This role involves developing and deploying reinforcement learning, optimization, and control models to solve complex business problems. The engineer will also be responsible for architecting the simulation platform and collaborating with data scientists and engineers to enhance platform policies and user experience.
Grab is Southeast Asia's leading superapp, providing services from food delivery to financial management and transport. We aim to drive Southeast Asia forward by economically empowering everyone through technology and AI, guided by heart, hunger, honour, and humility.
The Fulfilment Tech family is a crucial pillar for Grab, ensuring we serve our consumers and partners across various businesses and marketplaces in Southeast Asia. We develop high-throughput, real-time distributed systems using machine-learning techniques to process hundreds of millions of requests daily. Our mission is to enhance products and experiences for driver partners, increase adoption and engagement of services, improve driver partner opportunities and efficiency, and create efficient marketplaces with optimal, sustainable pricing for partners and consumers.
We are seeking a Senior Machine Learning Engineer reporting to the Senior Engineering Manager, for a hybrid work arrangement in Singapore. This is a hands-on role focused on building large-scale simulation platforms. You will construct a digital twin of Grab's marketplace, comprising tens of thousands of consumers, drivers, and merchants. Additionally, you will develop and deploy reinforcement learning, optimization, and control models to address business challenges within Grab's marketplace at scale.
The ideal candidate understands the software development life-cycle and engineering practices, possesses experience developing production ML systems, has worked on various regression, classification, and optimization problems, applied reinforcement learning (or control theory), and has experience with real-time streaming data.
Posted June 11, 2026