Capital One·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Capital One research engineer interview with a graph/optimization problem that took me a while to wrap my head around. The drone delivery problem sounds applied and cool until you realize it's basically a shortest path variant with constraints.

Questions Asked (1)

Design an algorithm to deliver a package from a start to a target location using drone flights and walking. Drones can only recharge at specific stations and have a limited range. Minimize the total walking distance the delivery person must cover.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was Dijkstra but I kept second-guessing whether to model walking distance or total distance as the edge weight.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where nodes represent locations (start, target, drone stations, and walking waypoints) and edges represent either drone flights (with range constraints and recharge stops) or walking segments. Then find the path that minimizes total walking distance, using a modified shortest path algorithm (e.g., Dijkstra) with state that includes current location and remaining drone battery. Discuss trade-offs between optimality and computational complexity, and consider practical constraints like drone speed and recharge time.

Pro tip: Clarify assumptions early: ask whether the delivery person can carry the drone while walking, whether multiple drones are available, and if walking can occur while the drone is in flight. This shows you think about real-world constraints and can adapt your algorithm accordingly.

1. Clarify requirements and constraints

Ask about drone range, recharge time, number of drones, whether the person must accompany the drone, and if walking and flying can happen simultaneously. This defines the problem scope.

2. Model as a graph problem

Create a graph where nodes are locations (start, target, stations, and points where the person can walk to). Edges represent drone flights (with range limits) and walking paths (with distances). Include battery level as part of the state if needed.

3. Define the optimization objective

Minimize total walking distance. Drone flight distance may be constrained by range and recharge stations, but walking distance is the cost to minimize. Consider if drone usage is free or has other costs.

4. Choose and adapt an algorithm

Use Dijkstra or A* with state (location, battery level) to find the path with minimum walking distance. If battery is continuous, discretize or use a multi-criteria approach. Discuss complexity and potential heuristics.

5. Discuss trade-offs and extensions

Talk about time vs. optimality, handling dynamic obstacles, multiple packages, or multiple drones. Mention how the solution scales and potential real-world implementation challenges.

Key Points to Mention

Graph representation with nodes for stations, start, target, and walking waypoints
State-space search including battery level or remaining range
Dijkstra's algorithm or A* for shortest path with constraints
Trade-offs between optimality and computational efficiency
Handling recharge stations as nodes with special transitions
Potential for dynamic programming or integer linear programming formulations

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