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DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

DoorDash coding interview with a sequence-removal problem that seems straightforward until you start thinking about the complexity. The naive solution gets you in the door but they clearly want you to push further.

Questions Asked (1)

Q1

Given a sequence of N items and a predicate that determines eligibility, repeatedly remove eligible items one at a time. After each removal, only the two neighbors of the removed item can become newly eligible. Return the remaining sequence or the number of removals. Optimize beyond the naive O(N^2) approach.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Started with the brute force and re-scanned the whole sequence after every removal.

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AI HintsAI Generated

Suggested Approach

Model the sequence as a doubly linked list to enable O(1) removal and neighbor access. Use a queue to process eligible items in order, checking only the two neighbors after each removal and enqueueing them if they become eligible. This yields O(N) time and O(N) space, avoiding the naive O(N^2) repeated scans.

Pro tip: Clarify upfront whether the predicate is static or dynamic; if dynamic, discuss how to handle re-evaluation. Also, mention edge cases like empty input, all eligible, or none eligible to show thoroughness.

1. Clarify requirements and constraints

Ask about input size, whether the predicate can change, and if the output should be the remaining sequence or just the count. Confirm that only immediate neighbors can become eligible after a removal.

2. Choose data structures

Use a doubly linked list to represent the sequence for O(1) removals and neighbor access. Use a queue (or stack) to process eligible items in the order they become eligible.

3. Outline the algorithm

Initialize the linked list and enqueue all initially eligible items. While the queue is not empty, dequeue an item, remove it from the list, and check its left and right neighbors; if they become eligible, enqueue them.

4. Analyze complexity and trade-offs

Explain that each item is enqueued at most once, so total operations are O(N). Compare with naive O(N^2) and discuss space trade-off (O(N) extra for queue and linked list pointers).

5. Handle edge cases and test

Consider empty input, all items eligible, none eligible, and cases where removals cascade. Walk through a small example to verify correctness.

Key Points to Mention

  • Doubly linked list for O(1) removal and neighbor access
  • Queue to manage eligible items in order of becoming eligible
  • Each item enqueued at most once, ensuring O(N) time
  • Space complexity O(N) for the linked list and queue
  • Comparison with naive O(N^2) approach and why it's inefficient
  • Edge cases: empty input, all eligible, none eligible, cascading removals

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