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Pinterest·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Pinterest SWE interview with a coding problem that boils down to heap usage. Pretty focused session, one problem with a complexity discussion tacked on at the end.

Questions Asked (1)

Q1

You have a list of pins, each with a height value. Distribute them across a fixed number of columns by always appending the next pin to the shortest column. Implement this and analyze the time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Once I saw 'always pick the shortest column' I knew it was a min heap and coded it up pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then propose an efficient solution using a min-heap to track column heights. Implement the algorithm, analyze its time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that this is a greedy algorithm and discuss how the choice of data structure (e.g., heap vs. sorted list) affects performance, showing awareness of trade-offs.

1. Clarify Requirements

Ask about input constraints, output format, and edge cases (e.g., empty list, zero columns, negative heights).

2. Design Algorithm

Propose using a min-heap to efficiently find the shortest column. Explain that each pin is appended to the column with minimum height, then that column's height is updated.

3. Implement Solution

Write code that initializes a heap with k zeros (for k columns), iterates through pins, pops the min, adds the pin height, and pushes back. Track column assignments if needed.

4. Analyze Complexity

State time complexity: O(n log k) due to heap operations for n pins and k columns. Space complexity: O(k) for the heap, plus O(n) if storing assignments.

5. Discuss Trade-offs

Compare with alternative approaches (e.g., linear scan for small k, balanced BST) and mention potential optimizations like using a custom heap or early termination.

Key Points to Mention

  • Greedy algorithm: always placing on the shortest column minimizes the maximum height.
  • Min-heap data structure for efficient retrieval of the shortest column.
  • Time complexity: O(n log k) where n is number of pins and k is number of columns.
  • Space complexity: O(k) for heap, O(n) if storing column assignments.
  • Edge cases: empty pin list, k=0, k > n, negative heights (if allowed).
  • Potential optimizations: using a sorted list for small k, or a bucket queue if heights are bounded.

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