← Pinterest Interview Insights

Pinterest·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Pinterest SWE interview with a masonry layout simulation problem. Pretty focused on getting the greedy assignment logic right and then talking through complexity. Nothing too wild but the problem had some subtle tie-breaking rules that could trip you up.

Questions Asked (1)

Q1

Given a list of pin heights and a fixed number of columns, simulate a masonry-style layout by assigning each pin to the column with the smallest current total height (breaking ties by column index). Return the final column assignments or total heights, and analyze time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The greedy part clicked pretty fast for me.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem requirements and edge cases, then propose an efficient solution using a min-heap to track column heights. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that a min-heap with (height, index) tuples ensures O(log k) per pin and handles tie-breaking naturally; also discuss how this scales for large numbers of pins and columns, and consider if a simpler approach suffices for small inputs.

1. Clarify requirements and edge cases

Ask about input constraints (e.g., number of pins, columns), output format (assignments or total heights), and edge cases like empty list or zero columns.

2. Choose data structure

Select a min-heap (priority queue) to efficiently retrieve the column with the smallest current height, storing (height, column_index) pairs.

3. Simulate placement

Iterate through each pin height, pop the smallest column from the heap, add the pin height to that column, record the assignment, and push the updated column back.

4. Analyze complexity

State time complexity O(n log k) for n pins and k columns, and space complexity O(k) for the heap and column heights.

5. Discuss trade-offs and optimizations

Compare with naive O(n*k) approach, mention potential optimizations like using a balanced BST or sorting if pins are pre-sorted, and discuss practical implications.

Key Points to Mention

  • Use a min-heap to track column heights for efficient minimum retrieval.
  • Tie-breaking by column index: ensure the heap comparison uses (height, index) to break ties correctly.
  • Time complexity: O(n log k) where n is number of pins and k is number of columns.
  • Space complexity: O(k) for the heap and column heights array.
  • Edge cases: empty pin list, zero columns, large inputs, and negative heights (if allowed).
  • Alternative approaches: naive O(n*k) simulation, or using a segment tree for range minimum queries.

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