Airbnb·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Airbnb coding round, one problem the whole time. It's a combinatorics-meets-data-structure question dressed up in product clothing, and I spent way too long on the naive approach before the interviewer nudged me toward something smarter.

Questions Asked (1)

Given a list of property listings, each with a set of available dates, and a desired stay range, find all pairs of listings whose combined availability fully covers the requested range so a guest could split their stay between the two.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was just iterate every pair and check coverage.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases, then propose an efficient algorithm that avoids brute-force O(n^2) comparison. For each listing, compute the gaps in its availability relative to the desired range, and use a hash map to find complementary listings that cover those gaps.

Pro tip: Mention that in production, availability data is often large and dynamic, so you'd consider indexing or precomputing coverage patterns to enable fast lookups, and discuss trade-offs between preprocessing time and query latency.

1. Clarify requirements and constraints

Ask about the size of the input, whether dates are discrete or continuous, if listings can have overlapping availability, and if the stay must be split into exactly two contiguous segments.

2. Define coverage and complement

For each listing, determine which parts of the desired range it covers and which parts it doesn't. The complement is the set of dates that must be covered by the other listing.

3. Design an efficient algorithm

Use a hash map to store listings keyed by their coverage pattern (e.g., a bitmask or interval representation). For each listing, look up its complement in the map to find valid pairs.

4. Handle edge cases and validate

Consider cases where one listing covers the entire range, where availability is non-contiguous, and where multiple listings share the same pattern. Validate with small examples.

5. Discuss trade-offs and optimizations

Compare time and space complexity of the proposed solution versus brute force. Mention potential optimizations like interval trees or precomputed indexes for large-scale systems.

Key Points to Mention

Time complexity: aim for O(n) or O(n log n) instead of O(n^2) by using hashing or interval trees.
Representation of availability: use bitmasks for small discrete ranges or interval lists for continuous ranges.
Complement calculation: efficiently compute the dates not covered by a listing within the desired range.
Handling overlapping availability: ensure that the union of the two listings' availability covers the entire range without gaps.
Scalability: discuss how to handle large datasets, possibly with indexing or caching.
Edge cases: single listing covering the whole range, no valid pairs, and non-contiguous availability.

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