LinkedIn
Interview Prep

Walk through: data model (graph with members as nodes, connections/interactions as edges), candidate generation (2nd-degree connections, shared employers, shared schools, location), feature engineering (connection strength, profile similarity, interaction recency), ranking model (gradient boosting or two-tower neural net), serving architecture (precomputed daily vs. real-time), and evaluation metrics (connection acceptance rate, not just CTR).

Walk through the recommendation context, the candidate set (what you were recommending from), the features you used for ranking, the model you chose and why, the offline and online evaluation approach, and the business outcome. Be specific about the metric you optimized — and whether it was the right metric in hindsight.

Use a structured product framework: define the user segment (active job seekers vs. passive browsers), map the current journey and identify the biggest friction points (application volume with no feedback, mismatched recommendations, long forms), propose a specific improvement with clear success metrics, estimate the opportunity size, and address trade-offs (recruiter experience vs. candidate experience).

LinkedIn is fundamentally a graph — 1B+ members, billions of connections, trillions of interaction events. Describe a graph data problem you worked on (social network analysis, knowledge graph, recommendation using graph traversal), the scale, the technical challenges (graph partitioning, query latency, storage efficiency), what you built, and the outcome.

LinkedIn Search is used for people, jobs, companies, and content. Walk through the system you improved: what was ranking poorly and why (feature gaps, training data bias, objective misalignment), what you changed (new features, new model, objective adjustment, query understanding improvement), how you measured relevance (human evaluation, NDCG, A/B test), and the outcome.

LinkedIn moderates content, fake profiles, spam InMails, and misleading job posts at scale. Describe a trust and safety or policy enforcement decision you were involved in: what the violation was, how you detected it, what the enforcement action was, and how you balanced false positives (blocking legitimate users) against false negatives (letting bad content through).

Walk through: signal collection (registration behavior, profile completeness patterns, connection velocity, IP/device fingerprinting, content patterns), feature engineering (behavioral vs. static features), model choice (gradient boosting for interpretability + neural nets for complex patterns), decision thresholds (human review vs. auto-action), and feedback loops for model improvement. Address the adversarial nature — bad actors adapt.

LinkedIn measures success in member engagement and economic graph growth. Walk through the feature, the hypothesis (why would this improve engagement?), how you instrumented and measured it, what the A/B test showed, and the outcome in terms of the metric that mattered. If the feature didn't work as expected, describe what you learned and what you tried next.