DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

DoorDash coding screen for a software engineer role. One problem, pretty domain-flavored but straightforward once you break it down.

Questions Asked (1)

Given an order with a pickup location and a list of dashers (each with an id, current location, and availability flag), write a function that assigns the order to the nearest available dasher by Euclidean distance, breaking ties by smallest id. Return the dasher's id, or null if no one is available. Also analyze the time complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The logic itself isn't hard but I spent a weird amount of time second-guessing whether I needed to actually sort or just do a linear scan.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and edge cases, then propose a single-pass linear scan that filters available dashers and tracks the minimum distance with tie-breaking by id. After coding, analyze the time complexity as O(n) and space complexity as O(1), and discuss potential optimizations for repeated queries.

Pro tip: Mention that for a one-time query, a linear scan is optimal, but for many orders, a spatial index like a k-d tree or geohash could reduce query time to O(log n). This shows you think about scalability and real-world system design.

1. Clarify requirements and edge cases

Ask about input types, coordinate system, tie-breaking rules, and what to return if no dasher is available. Confirm whether dashers can be reused or if availability changes.

2. Design the algorithm

Propose iterating through the list once, skipping unavailable dashers, and computing squared Euclidean distance to avoid floating-point errors. Track the best dasher by comparing distance and then id.

3. Implement the solution

Write clean code with a single loop, initializing best distance to infinity and best id to null. Update when a closer dasher is found or when distances are equal and the id is smaller.

4. Analyze complexity and trade-offs

State that time complexity is O(n) and space is O(1). Discuss that for multiple queries, preprocessing with a spatial index could improve performance, but adds overhead.

5. Test with examples

Walk through a few test cases: no available dashers, one available, multiple with ties, and negative coordinates. Verify the tie-breaking logic.

Key Points to Mention

Use squared Euclidean distance to avoid unnecessary square root operations and floating-point precision issues.
Tie-breaking by smallest id: ensure the comparison checks distance first, then id.
Time complexity O(n) for a single query; space complexity O(1).
Edge cases: empty list, all unavailable, null pickup location.
Potential optimization for repeated queries: spatial indexing (e.g., k-d tree, geohash) to achieve O(log n) query time.
Clarify assumptions about coordinate system and distance metric (e.g., Euclidean vs. Manhattan).

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