ShareChat
Indic social and short-video apps (ShareChat, Moj); ML for feed ranking
Interview in a few hours? A quick sprint for ShareChat
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How ShareChat usually runs it for applied AI and LLM engineer roles.
- Focus: Motivation, ML background
Recruiter screen
A 30-45 minute call on your ML background, why ShareChat, and practical details like notice period and pay.
- Focus: DSA under time pressure
Online assessment
A timed HackerEarth test with a few coding problems that range from medium to hard.
- Focus: Problem solving, coding
DSA round
A live round of data-structure problems where you talk through the approach before coding it.
- Focus: Recsys design, ML depth
ML depth and ML system design
Technical rounds with senior ML engineers on ML fundamentals and an end-to-end design of a recommendation or feed-ranking system at ShareChat's scale, including experimentation and deployment.
- Focus: Ownership, culture fit
Hiring manager and director rounds
A manager deep-dive into your current project's architecture and scaling, then a director conversation on career path and fit.
How it weights your plan
Selecting ShareChat when you build a plan multiplies each topic’s priority score:
Official prep and resources
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