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Airbnb·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Airbnb software engineer interview with a coding problem centered on listing availability and date range coverage. The problem felt domain-specific to Airbnb's product, which was a nice touch, but the bit-mask optimization angle is where things got interesting.

Questions Asked (1)

Q1

Given a dictionary mapping listing names to lists of available day numbers, and a target date range, return all combinations of two distinct listings whose combined availability fully covers the range. Also return any single listing that covers the range on its own.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The singleton case tripped me up at first.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm using bitmasks or sets to represent availability. Precompute coverage for each listing, then check singles and pairs for full coverage, discussing time/space trade-offs.

Pro tip: Mention that using bitmasks allows O(1) coverage checks and fast intersections, but be prepared to discuss memory trade-offs for large ranges. Also, consider sorting or hashing to avoid redundant pair checks.

1. Clarify requirements and constraints

Ask about input size, date range representation (inclusive/exclusive), and whether listings can have overlapping availability. Confirm output format and if order matters.

2. Choose data representation

Decide between sets, bitmasks, or interval lists based on range size and density. Bitmasks are efficient for small ranges; sets or interval trees for large sparse ranges.

3. Preprocess listings

For each listing, compute its coverage as a bitmask or set, and filter out those with no overlap with the target range. Identify singles that fully cover the range.

4. Find valid pairs

Iterate over pairs of listings, checking if the union of their coverage equals the target range. Use bitwise OR for bitmasks or set union for sets, and optimize by skipping pairs that cannot cover.

5. Analyze complexity and trade-offs

Discuss time complexity (O(n^2) for pairs, but can be optimized with indexing) and space complexity. Mention alternative approaches like interval merging or using a hash map of missing days.

Key Points to Mention

  • Use bitmasks for O(1) coverage checks and fast unions, but note memory usage for large ranges.
  • Precompute coverage for each listing to avoid redundant calculations.
  • Handle edge cases: empty listings, no coverage, duplicate listings, and target range of length 0.
  • Optimize pair search by grouping listings by their coverage pattern or using a hash set of missing days.
  • Discuss time complexity: O(n^2) naive, but can be improved with indexing or meet-in-the-middle.
  • Consider scalability: for very large date ranges, use interval trees or sorted lists of intervals.

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