DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

DoorDash coding screen with a custom array simulation problem. The problem looked like a heap question at first glance but had enough edge cases to slow me down.

Questions Asked (1)

Given an array of distinct positive integers, repeatedly find all 'eligible' elements (those greater than their current neighbors, with boundary elements only needing to beat their single neighbor), pick the smallest eligible element, remove it, and continue until the array is empty. Return the order in which elements were removed.

Algorithms & Data Structures

Author's notes

My first instinct was to reach for a heap and call it done.

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AI HintsAI Generated

Suggested Approach

Clarify the problem with examples and edge cases, then propose an efficient algorithm using a priority queue to track eligible elements and a doubly linked list to manage removals. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Mention that after removing an element, only its immediate neighbors can become newly eligible, so you only need to update those, avoiding a full scan. This shows you understand the problem's locality property and can optimize accordingly.

1. Understand the problem

Restate the problem in your own words, walk through a small example, and clarify edge cases such as single-element arrays or when multiple elements are eligible.

2. Design the data structures

Use a doubly linked list to represent the array and allow O(1) removals, and a min-heap to efficiently retrieve the smallest eligible element.

3. Outline the algorithm

Initialize the heap with all initially eligible elements. Repeatedly pop the smallest, remove it from the linked list, and check if its neighbors become eligible; if so, push them into the heap.

4. Analyze complexity

Explain that each element is inserted and removed from the heap at most once, giving O(n log n) time and O(n) space.

5. Discuss edge cases and optimizations

Cover cases like all elements eligible initially, and mention that only neighbors need re-evaluation, which is already handled by the algorithm.

Key Points to Mention

Use a min-heap to efficiently get the smallest eligible element.
Use a doubly linked list for O(1) removal and neighbor access.
Only neighbors of a removed element can become newly eligible.
Time complexity: O(n log n) due to heap operations.
Space complexity: O(n) for the heap and linked list.
Handle edge cases like single-element array and duplicate eligibility checks.

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