Coinbase·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Coinbase software engineer interview with a simulation-style coding problem. The drone delivery question looked deceptively simple at first but the follow-ups they mentioned made me realize I barely scratched the surface of what they were actually testing for.

Questions Asked (1)

You're building a 1D drone delivery simulation. A package starts at position 0 and needs to reach a target. Charging stations are given as a sorted array. The drone can fly at most 10 units per hop, and a human has to walk the package from its current position to the nearest station that's at or ahead of it before each hop. Return the total walking distance across all hops.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I spent way too long trying to be clever about this.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the problem statement and edge cases, then propose a greedy algorithm that simulates each hop: from the current position, find the nearest charging station at or ahead, add the walking distance, and jump up to 10 units forward. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Demonstrate strong communication by walking through a small example step-by-step, and explicitly state assumptions (e.g., target may not be a station, stations are sorted). This shows attention to detail and prevents misunderstandings.

1. Clarify the problem

Ask questions to confirm details: Is the target guaranteed to be reachable? Can the drone fly less than 10 units? Are stations inclusive? What if no station is ahead? This ensures you understand the requirements.

2. Outline the greedy simulation

Explain that at each step, you find the nearest station at or ahead of the current position, add the distance to total walking, then move the drone forward by up to 10 units (or to the target if closer). Repeat until target is reached.

3. Analyze complexity and edge cases

State that the algorithm runs in O(n) time and O(1) space, where n is the number of stations. Discuss edge cases: target before first station, no stations, target exactly at a station, etc.

4. Discuss trade-offs and optimizations

Mention that the greedy approach is optimal because walking to the nearest station minimizes walking distance per hop. Consider if there are scenarios where walking further could reduce total distance (e.g., skipping a station) and explain why not.

5. Test with examples

Walk through a concrete example, such as stations at [5, 15, 25] and target 30, to verify the algorithm and demonstrate correctness.

Key Points to Mention

Greedy strategy: always walk to the nearest station at or ahead to minimize walking distance.
Simulation loop: iterate through stations, tracking current position and total walking distance.
Time complexity O(n) and space complexity O(1), where n is the number of stations.
Edge cases: target before first station, no stations, target exactly at a station, unreachable target.
Proof of optimality: walking to a farther station would only increase walking distance without benefit.
Communication: clarify assumptions and walk through an example to ensure alignment.

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