Grab
Southeast Asian superapp that calls itself an AI-first company
Interview in a few hours? A quick sprint for Grab
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Grab usually runs it for applied AI and LLM engineer roles.
- Focus: Background and motivation
Recruiter screen
An exploratory 30-60 minute conversation with a recruiter about your background, goals and motivation.
- Focus: Data structures and algorithms
Online coding test
A timed online assessment with a few easy-to-medium algorithm problems, used as an early filter for engineering roles.
- Focus: DSA, problem-solving fundamentals
Live coding
One or two coding problems solved in real time on an online platform while you explain your approach.
- Focus: Scalable architecture, trade-offs
System design
A discussion or whiteboard session designing a scalable system, covering architecture, security and monitoring, with your choices justified.
- Focus: Values and collaboration
Culture fit
A conversation about collaboration and how your values match Grab's 4Hs: Heart, Hunger, Honour and Humility; leads also get a leadership round.
Topics to prepare for Grab
The topics its loop weighs most, each with what interviewers check and what to study.
- GenAI system designSystem design
- Cost and latency engineering for LLM appsSystem design
- RAG vs fine-tuning vs prompting: choosing the approachSystem design
- Design an AI customer-support agentSystem design
- Design enterprise document search / a knowledge assistantSystem design
- Behavioral interviews: STAR stories for AI engineersBehavioral
- Python for ML interviewsCoding
- Calling LLM APIs in Python: async, streaming, retriesCoding
How it weights your plan
Selecting Grab when you build a plan multiplies each topic’s priority score:
Official prep and resources
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