Meta's system-design rounds famously use its own products as the canvas: design News Feed, design a photo store like the one behind Instagram, design Messenger.
In 2026 the bar is a clean separation between candidate generation, ranking, and delivery, plus a real decision on fan-out on write versus fan-out on read for feeds and messaging. Interviewers push on scale numbers, consistency and ordering guarantees, and the hot-path latency budget. They reward candidates who state assumptions, do quick capacity math, and defend one design over the alternatives rather than listing every option.
About Meta
Meta's Bengaluru office works on ads infrastructure, WhatsApp, Instagram, and Reality Labs (VR/AR). Known for a bar-raiser process comparable to Amazon's, with a strong bias toward candidates who can solve at scale.
Recruiter screen and coding phone screen
Onsite coding rounds on DSA
System-design round on a Meta-style product
Behavioral round on impact and collaboration, then debrief
Round 1 (45 min)
coding phone screen on data structures and algorithms.
Round 2-3 (45 min each)
onsite coding rounds.
Round 4 (45 min)
system-design round on feed, storage, or messaging.
Round 5 (45 min)
behavioral round on impact and working with others.
Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.
The typical Meta recruitment process has 4 stages: Recruiter screen and coding phone screen → Onsite coding rounds on DSA → System-design round on a Meta-style product → Behavioral round on impact and collaboration, then debrief.
Meta typically conducts 4 interview rounds: Round 1 (45 min): coding phone screen on data structures and algorithms.; Round 2-3 (45 min each): onsite coding rounds.; Round 4 (45 min): system-design round on feed, storage, or messaging.; Round 5 (45 min): behavioral round on impact and working with others..
HireStepX recommends the Generate-Rank-Deliver framework for this type of interview: Split the problem into candidate generation, ranking, and delivery, decide fan-out on write vs read explicitly, and defend the latency and consistency trade-offs with rough capacity math
To answer this question well, HireStepX recommends the Generate-Rank-Deliver approach: Split the problem into candidate generation, ranking, and delivery, decide fan-out on write vs read explicitly, and defend the latency and consistency trade-offs with rough capacity math Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Generate-Rank-Deliver approach: Split the problem into candidate generation, ranking, and delivery, decide fan-out on write vs read explicitly, and defend the latency and consistency trade-offs with rough capacity math Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Generate-Rank-Deliver approach: Split the problem into candidate generation, ranking, and delivery, decide fan-out on write vs read explicitly, and defend the latency and consistency trade-offs with rough capacity math Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Generate-Rank-Deliver approach: Split the problem into candidate generation, ranking, and delivery, decide fan-out on write vs read explicitly, and defend the latency and consistency trade-offs with rough capacity math Ground your answer in a specific real example from your own experience.
Design News Feed, a photo storage and serving system, a real-time messaging system like Messenger, and typeahead search, usually at billions-of-users scale.