Netflix·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Netflix system design round focused entirely on one beast of a question about rendering the home page above-the-fold with deduplication across rows. Dense problem, lots of moving parts, and I felt like I was playing catch-up the whole time.

Questions Asked (1)

Design the above-the-fold rendering for the Netflix home page such that every show visible in the initial viewport is unique across all rows and within each row. Walk through the data model, personalization inputs, constraints like editorial pins and maturity ratings, the deduplication algorithm, conflict resolution, backfill strategy, caching, lazy loading, latency budget, and how you'd measure success.

System DesignData ModelingA/B Testing & Experimentation

Author's notes

This is one of those questions where you think you have a handle on it after the first minute and then the interviewer starts pulling threads.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that separates data retrieval, deduplication, and rendering. Walk through the deduplication algorithm step-by-step, addressing edge cases like editorial pins and maturity ratings, and explain how you'd handle caching, lazy loading, and latency. Finally, discuss how you'd measure success through A/B testing and metrics.

Pro tip: Emphasize that deduplication should happen server-side to minimize client-side processing and ensure consistency across devices. Also, mention that you'd use a greedy algorithm with backtracking for conflict resolution, and consider precomputing personalized rows during off-peak hours to meet latency budgets.

1. Clarify Requirements and Constraints

Ask questions to understand the scope: number of rows, typical row sizes, definition of 'above-the-fold', and how editorial pins and maturity ratings interact with deduplication. Confirm that uniqueness is required across all rows and within each row.

2. Design Data Model and Personalization Inputs

Outline the data structures: shows with metadata (ID, maturity rating, editorial flags), user profiles with viewing history and preferences, and row definitions (e.g., 'Trending', 'Because You Watched'). Explain how personalization scores are computed and combined with editorial pins.

3. Develop Deduplication Algorithm

Propose a greedy algorithm that iterates through rows in priority order, selecting the highest-scored show that hasn't been used yet. For conflicts (e.g., editorial pins that violate uniqueness), use backtracking or swap with next best candidate. Ensure maturity ratings are respected.

4. Address Backfill, Caching, and Latency

Describe backfill strategy: if a row can't be filled uniquely, relax constraints (e.g., allow duplicates within row but not across) or pull from fallback rows. Discuss caching personalized rows in a fast key-value store (e.g., EVCache) and lazy loading below-the-fold rows to meet a latency budget (e.g., <100ms for above-the-fold).

5. Define Success Metrics and A/B Testing

Propose metrics: engagement (click-through rate, play rate), diversity of content consumed, and user satisfaction. Design A/B tests comparing deduplicated vs. non-deduplicated layouts, measuring impact on retention and session time.

Key Points to Mention

Server-side deduplication to ensure consistency and reduce client load
Greedy algorithm with backtracking for conflict resolution
Handling editorial pins by treating them as high-priority constraints
Maturity rating filtering as a hard constraint in selection
Caching personalized rows with TTL and invalidation on new content
Latency budget: precompute during off-peak, use CDN for static assets, and lazy load below-the-fold

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.