Backend Engineer, Machine Learning

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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 the Team

The Fulfilment tech family is one of the most important pillars enabling Grab to out-serve our customers 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 fulfil customer 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 customers. Currently our team members are in Indonesia, Malaysia and Singapore.

Get to know the Role:

We are seeking talented & passionate Engineers to join our team. You will have opportunities to work with a small team of engineers to work on multiple backend service clusters as well as participate in building machine learning pipelines. It is very important that our team members take the initiatives to identify problems and have the right mindset and skill sets to solve them.

The day-to-day activities:

• Engage in service capacity and demand planning, software performance analysis, tuning, and optimization
• Collaborate with product and experience teams to finalise feature specifications, build prototype and design experiments.
• Work with different engineering teams to explore and create new design/architectures geared towards scale and performance
• Participate in code and design reviews to maintain our high development standards
• Join oncall rotations to debug production issues and improve system stability.
• Design and write with the cutting edge GO language to improve the availability, scalability, latency, and efficiency of Grab's range of services
• Lead project development as engineering owner of a group of 3~5 engineers, working closely with product managers to understand the requirements, propose solutions and coordinate dependencies
• Work closely with infrastructure team in building and scaling back-end services as well as performing root cause analysis investigations
• Work closely with mobile team to build reusable modularized mobile components utilising scalable APIs

Technical must-haves:

• You can be a good coder in any language (C++, C, Java, Scala, Rust, Haskell, OCaml, Erlang, Python, Ruby, PHP, Node.JS, C#, etc.), but willing to work on Golang and Scala (Flink)
• Hands-on familiarity with CI/CD pipelines for system development and deployment
• Hands-on familiarity with running large scale distributed web or api services; understanding of systems internals and networking is a plus
• Strong understanding and experience on cloud platforms like AWS, GCP, Azure
• Strong computer science fundamentals in algorithms and data structures
• Working experience on backend development for 1~3 years
• Strong understanding of system performance and able to do profiling to find system bottlenecks
• Experience with high-speed distributed computing frameworks like Apache Flink.
• Strong understanding of testing frameworks for unit testing, integration testing and E2E testing
• Hands-on familiarity with database and at least one data query languages like mysql or Presto

Soft skills must-haves:

• Fluent in spoken and written English
• Good communication skills, and have an proactive mindset
• Able to think critically of the current system in terms of growth and stability

Nice-to-haves:

• Experience with Kubernetes, Dockers is a plus.
• A degree in Computer Science, Software Engineering, Information Technology or related fields.
• Experience with building machine learning pipelines, optimising model performance.
• Experience with geospatial based algorithms and APIs like OpenStreet Maps or Google Maps.