← Ziprecruiter Interview Insights

Ziprecruiter·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Ziprecruiter SWE interview that leaned hard into data structures and design tradeoffs. The main problem was building a voting system from scratch and defending every decision you made along the way.

Questions Asked (1)

Q1

Design and implement a hash-table-backed data structure for a restaurant voting system that supports vote(restaurant), unvote(restaurant), and getMostPopular(), tracking the top restaurant in real time.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I started with the obvious hashmap of restaurant to count, which they were fine with, but then getMostPopular tripped me up.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a hash map for vote counts paired with a max-heap or bucket structure for real-time top tracking. Discuss trade-offs between update time and query time, and walk through the implementation of each operation.

Pro tip: Mention that you would use lazy deletion or a versioned heap to handle unvote efficiently without O(n) removals, and note that getMostPopular can be O(1) if you maintain a sorted structure or track the max on each update.

1. Clarify Requirements and Constraints

Ask about expected scale, frequency of operations, and whether getMostPopular must be O(1) or can be O(log n). Confirm if ties need special handling and if restaurant names are unique.

2. Design Core Data Structures

Propose a hash map (restaurant -> vote count) for O(1) vote/unvote updates. For top tracking, consider a max-heap with lazy deletion, a balanced BST, or a bucket sort approach.

3. Implement Operations

Detail vote: increment count and update top structure. Unvote: decrement count and mark heap entry as stale or update BST. getMostPopular: return top from structure, cleaning stale entries if needed.

4. Analyze Complexity and Trade-offs

Compare time/space for each approach: heap gives O(log n) updates and O(1) getMostPopular with lazy deletion; BST gives O(log n) for all; bucket sort gives O(1) updates but O(n) getMostPopular. Choose based on requirements.

5. Handle Edge Cases and Optimizations

Discuss ties, unvoting non-existent restaurants, and concurrency if needed. Mention potential optimizations like caching the top or using a doubly linked list for O(1) updates in specific scenarios.

Key Points to Mention

  • Hash map for O(1) vote count updates
  • Max-heap with lazy deletion for efficient top tracking
  • Trade-offs between update and query time complexities
  • Handling unvote without O(n) removal
  • Edge cases: ties, invalid unvotes, and empty state
  • Scalability and concurrency considerations

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