LinkedIn's system-design rounds revolve around its social graph and professional feed: design 'People You May Know', design 'who viewed your profile', design a feed blending posts, jobs, and recommendations.
In 2026 strong candidates lead with graph storage and second-degree traversal, decide what to precompute offline versus serve online, and handle write-heavy event streams like profile views. Interviewers probe how you rank and dedupe heterogeneous content and how you keep latency low while the graph grows. Vague 'use a graph database and it scales' answers get taken apart in the follow-ups.
About LinkedIn
LinkedIn India (Bengaluru) is a Tier-1 Microsoft subsidiary engineering hub covering feed ranking, AI recommendations, Sales Navigator infrastructure, and identity systems. The campus contributes directly to LinkedIn's global product.
Recruiter screen and coding phone screen
Onsite coding rounds on DSA
System-design round on a graph or feed problem
Hiring-manager and values round, then offer
Round 1 (45 min)
coding phone screen on data structures and algorithms.
Round 2-3 (45 min each)
onsite coding rounds.
Round 4 (45-60 min)
system-design round on graph or feed problems.
Round 5 (45 min)
hiring-manager round on ownership and collaboration.
Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.
2 more questions. Sign up to unlock all
Sign up free: unlock all questionsThe typical LinkedIn recruitment process has 4 stages: Recruiter screen and coding phone screen → Onsite coding rounds on DSA → System-design round on a graph or feed problem → Hiring-manager and values round, then offer.
LinkedIn 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 (45-60 min): system-design round on graph or feed problems.; Round 5 (45 min): hiring-manager round on ownership and collaboration..
HireStepX recommends the Graph-and-Precompute framework for this type of interview: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads
To answer this question well, HireStepX recommends the Graph-and-Precompute approach: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Graph-and-Precompute approach: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Graph-and-Precompute approach: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Graph-and-Precompute approach: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Graph-and-Precompute approach: Model the social graph explicitly, decide what to precompute offline versus serve online, and design write-heavy event ingestion separately from low-latency reads Ground your answer in a specific real example from your own experience.