Airbnb·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Airbnb software engineering interview with a combinatorics/greedy algorithm problem focused on property selection by neighborhood and capacity constraints. Pretty clean problem once you see the structure, but the tie-breaking rules added some real complexity.

Questions Asked (1)

Given a list of properties (each with an ID, neighborhood, and capacity), a target neighborhood, and a group size, return the IDs of a combination of properties from that neighborhood whose total capacity meets or exceeds the group size. Minimize the number of properties selected, and among ties on count, minimize total capacity. Return an empty list if no feasible combination exists.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The filtering step is obvious, sort the neighborhood's properties by capacity descending and greedily pick the largest ones until you hit the target.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a solution that filters properties by the target neighborhood and finds the minimal number of properties whose total capacity meets or exceeds the group size. Use a greedy approach by sorting capacities in descending order to minimize count, and for ties, consider combinations that minimize total capacity, possibly using dynamic programming or a search strategy.

Pro tip: Discuss the trade-offs between greedy and optimal solutions, and mention that while greedy minimizes count, it may not always minimize total capacity for ties; a DP approach can guarantee optimality but may be less efficient for large inputs.

1. Clarify Requirements and Constraints

Ask about input size, whether capacities are positive integers, if multiple combinations are possible, and how to handle ties. Confirm that the goal is to minimize count first, then total capacity.

2. Filter and Sort Properties

Filter the list to only include properties in the target neighborhood. Sort these properties by capacity in descending order to facilitate a greedy selection for minimizing count.

3. Find Minimal Count Combination

Use a greedy approach: select the largest capacities until the sum meets or exceeds the group size. This gives the minimal number of properties. If no combination meets the group size, return an empty list.

4. Optimize for Tie-Breaking (Minimize Total Capacity)

Among combinations with the minimal count, find the one with the smallest total capacity. This may require exploring alternative combinations, such as using dynamic programming or a search over subsets of the minimal size.

5. Return Result and Discuss Complexity

Return the list of property IDs. Analyze time and space complexity, and discuss potential optimizations or alternative approaches for large datasets.

Key Points to Mention

Greedy algorithm for minimizing count: sort by capacity descending and pick largest until sum >= group size.
Tie-breaking: minimizing total capacity may require considering combinations of the same minimal size, not just the greedy selection.
Dynamic programming or backtracking for optimal tie-breaking, with trade-offs in time complexity.
Edge cases: no properties in neighborhood, group size larger than sum of all capacities, empty list input.
Time and space complexity analysis: O(n log n) for sorting, O(2^n) for brute force, O(n * group size) for DP.
Communication: explain thought process, ask clarifying questions, and discuss trade-offs between approaches.

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