DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

DoorDash coding round focused almost entirely on one active-point distance problem, but they squeezed two follow-up variants out of it so it felt longer than it was. Not a bad experience, just dense.

Questions Asked (1)

Given a stream or array of points that can each be marked active or inactive, how would you efficiently answer distance queries to the nearest active point?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one shows up a lot apparently and I still fumbled the extension.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints: are points static or dynamic, what is the query volume, and what are the latency requirements? Then propose a solution that balances preprocessing and query time, such as a spatial index (e.g., KD-tree, quadtree) for static points or a dynamic data structure (e.g., Voronoi diagram with dynamic updates) for streaming points. Discuss trade-offs between update and query costs, and consider approximations if exact answers are too slow.

Pro tip: Mention that in real-world systems like DoorDash, approximate nearest neighbor (ANN) with locality-sensitive hashing (LSH) or a grid-based approach is often preferred over exact methods due to the need for low-latency at scale, and that you would validate the trade-off with actual latency and accuracy metrics.

1. Clarify requirements and constraints

Ask about the nature of the points (static vs. dynamic), query frequency, expected number of points, and latency/accuracy requirements. This determines whether an exact or approximate solution is appropriate.

2. Choose a data structure

For static points, consider building a KD-tree or Voronoi diagram for efficient nearest neighbor queries. For dynamic points, consider a grid-based index or a dynamic Voronoi diagram with incremental updates.

3. Analyze time and space complexity

Compare the preprocessing time, query time, and update time for each option. For example, KD-tree gives O(log n) query for static points but O(n) update; grid-based gives O(1) query but may require tuning cell size.

4. Consider trade-offs and alternatives

Discuss approximations like LSH or random projections if exact queries are too slow. Also consider hybrid approaches, such as maintaining a coarse grid for quick filtering and then refining with exact distance calculations.

5. Propose a concrete solution and justify

Based on the clarified requirements, recommend a specific approach, explain why it fits, and mention potential optimizations or fallbacks. For example, for a high-query, low-update scenario, a KD-tree with periodic rebuilds might be ideal.

Key Points to Mention

Spatial indexing structures: KD-tree, quadtree, R-tree, and their trade-offs.
Dynamic updates: how to handle point activation/deactivation efficiently (e.g., lazy deletion, rebuilding).
Approximate nearest neighbor algorithms (LSH, Annoy, FAISS) for scalability.
Grid-based partitioning and its simplicity for uniform distributions.
Voronoi diagrams for exact nearest neighbor queries in static settings.
Time-space trade-offs and the impact of dimensionality (curse of dimensionality).

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