Grab Hiring! Full Time Senior Software Engineer, Backend in Selangor - Ricebowl

Get to Know the Team

The SNP team builds the personalization platforms that power every business vertical across Grab, serving millions of consumers, merchants and partners in Southeast Asia. Our mission is to help consumers discover and enjoy maximum value through engaging experiences and offers, while driving sustainable growth for merchant partners, Grab, and multiple businesses. Large-scale personalization is how we cut through choice overload — surfacing the most relevant meals, services and offers in seconds to lift conversion and engagement, while delivering tailored value that grows merchant and partner revenue. Our systems are among the most used in Grab, involved in virtually every transaction made, and we operate with an experimental mindset where the customer decides what works. The team members are located in China, Malaysia and Singapore.

Get to know the Role

You are expected to take strong ownership — proactively identifying problems, framing them clearly, and driving them to resolution with minimal guidance. You take end-to-end accountability for the systems you own, anticipate failures and design fallbacks before they surface, and consistently raise the engineering bar for the people around you. You bring hands-on experience building and operating recommendation systems, with a solid understanding of recommendation design and optimization across the full funnel, familiarity with model architectures and feature engineering, and a working knowledge of machine learning.

You will be reporting to the Software Engineering Manager II and will be base onsite at our Petaling Jaya, Selangor, Malaysia office.

The Critical Tasks You Will Perform

• You will oversee the end-to-end recommendation pipeline — candidate generation, pre-ranking, and ranking — leading both iterative improvements and innovations.
• You will design and write in the Go language to improve the availability, scalability, latency, and efficiency of the team's range of services.
• You will work with product and design teams to understand requirements, provide product feedback, and own and implement features from start to finish.
• You will partner with Data Science to improve our ML infrastructure and feature engineering pipelines.
• You will participate in technical discussions, code reviews, and design reviews with other engineering teams, providing constructive feedback and striving to raise the engineering bar.
• You will write code that is reusable, scalable and testable, and collaborate with QA to ensure products are delivered on time, with high quality, and deployed stably.
• You will participate in service capacity and demand planning, software performance analysis, tuning, and optimization.
• You will be responsible and responsive during on-call duty, debugging and troubleshooting production issues to help us achieve operational excellence.
• You will provide technical guidance, mentorship, and knowledge sharing to peers.

What Essential Skills You Will Need

• You have a Bachelor in Computer Science or equivalent, with good fundamentals in algorithms and data structures.
• You have at least 5+ years of experience in software engineering in a distributed systems environment.
• You have experience shipping products (such as backend services) to production, with an understanding of system performance and scaling.
• You have hands-on experience with recommendation systems and are familiar with their design and optimization, model architectures, and feature engineering, with a working knowledge of machine learning.
• You can be a good coder in any language but are willing to work on Golang.
• You have knowledge of cloud technologies and experience developing and deploying services on the cloud.
• You have an investigative mindset and the ability to identify system bottlenecks, together with an enthusiasm for innovation and continuous improvement.

   

  Good-to-Have:
• Proficiency in Golang and Python languages would be advantageous.
• Experience optimizing large-scale ML serving infrastructure would be advantageous too.