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Life at Grab
At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.
Get to know our Team
The Fulfillment tech family is one of the pillars enabling Grab to out-serve our consumers and partners in different businesses and marketplaces across Southeast Asia. We are working on high throughput, real-time distributed systems that use sophisticated machine learning techniques to solve hundreds of millions of requests per day. Our mission is to offer the best-in-class products and experiences to our driver partners as to increase adoption and engagement of our services. Improve driver partner opportunities and efficiency in order to fulfill consumer orders without fail, rain or shine. And to create efficient marketplaces by determining an optimal price that is both sustainable and loved by our partners and consumers.
Grab is Southeast Asia’s leading super-app. We provide everyday services such as deliveries, mobility, financial services, enterprise services and others to millions of users across the region. At the fulfillment machine engineering team, we are trying to solve challenging problems in the marketplace that involve dynamic pricing, supply and demand management. We are looking for senior machine learning engineers to join the team to help us make that vision a reality by developing and refining cutting-edge reinforcement learning models and simulation platforms.
Get to know the Role
This is a hands-on role involving building large-scale simulation platforms and reinforcement algorithms. You will have the opportunity to build a digital twin of Grab’s marketplace that consists of tens of thousands of consumers, drivers and merchants. Furthermore, you will have the opportunity to develop reinforcement learning, optimization and control models to solve business problems inside Grab’s marketplace and deploy them at scale.
The ideal candidate will have solid understanding of software development life-cycle and engineering practices, experience developing production ML systems, experience working on a range of regression/classification and optimization problems, experience applying reinforcement learning (or control theory), experience working with real-time streaming data.
The day-to-day activities
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