Airbnb Interview Questions — Page 13

Airbnb·Software Engineer·Technical Phone Screen

Jun 2026

Airbnb software engineer interview with a grid-based scoring problem that looks deceptively simple until you actually try to write test cases for it yourself.

You're given a 5x5 board where each cell encodes a terrain type and a crown count (e.g. 'G1' for grass with 1 crown). Find every maximal 4-directionally-connected region of cells with the same terrain type, then for each region multiply the total crowns in it by the region's size. Return the sum across all regions. Also write your own test cases.

“The algorithm itself is basically flood fill, BFS or DFS, not too bad.”

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Airbnb·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Airbnb system design round for a software engineer role. One question, chat app, classic but not easy to do well under pressure.

Design a chat application.

“Spent the first few minutes going too broad, like I was trying to design WhatsApp from scratch, and the interviewer had to nudge me to scope it down.”

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Airbnb·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Airbnb product sense question focused on their Open Homes program for refugee housing. Single question, no frills, just a straight product improvement case.

How would you improve Airbnb's Open Homes product, which offers free housing to refugees?

“This one tripped me up more than I expected.”

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Airbnb·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026

Airbnb ML Engineer coding round. They give you a set of files and you have to implement fit and transform methods from scratch, then get the whole thing running. Two phases: debugging first, then optimization. Pretty manageable if you write production Python regularly.

Given a set of provided files, implement the fit and transform methods from scratch and get the code running within the time limit.

“Two phases split across the session, roughly 25 minutes to debug and 35 to optimize.”

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Airbnb·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Airbnb behavioral round for a software engineering role, focused entirely on learning agility and how you handle criticism. Three stories, back to back. Not the most technically demanding session but the kind where you realize mid-answer that your examples aren't as strong as you thought they were.

Walk me through a time you taught yourself something new: what you learned, how you structured the process, and how it showed up in your actual work afterward.
Tell me about a time you fell short of expectations and then used feedback from customers to change your approach.
Describe a time you received critical feedback from someone and how you responded to it.

“I had a decent story here but I front-loaded too much on the 'what I learned' part and barely got to the impact on my work.”

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Airbnb·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Airbnb system design round focused entirely on building a group chat backend, the kind of thing you'd see in their host-guest messaging product. Covered a lot of ground: storage, real-time delivery, unread counts, the whole thing. Felt like a reasonable interview but there were a few moments where I got lost in the weeds.

Design the backend for a real-time group chat system supporting multi-party conversations, message history, and unread counts at scale.
What clarifying questions would you ask before designing a group chat system, and how do the answers change your design?
Walk through the data model for a chat system and explain how you'd guarantee message ordering within a conversation.
How would you design real-time message delivery to online users across millions of concurrent connections?
How do you track unread message counts per user per conversation efficiently, and how do you keep them accurate as messages are read?
How do you handle message history pagination efficiently as the conversation grows?
How would you guarantee a message is never lost if the server crashes after acknowledging the sender but before fan-out completes?
How would your design change if group conversations could have 100,000+ members instead of a few hundred?
How would you implement message edit, delete, and recall while keeping every participant's view consistent?

“This is a big one and I underestimated how much the interviewer wanted me to decompose it upfront.”

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Airbnb·Software Engineer·Technical Phone Screen

Jun 2026

Airbnb SWE interview with a pretty gnarly algorithmic problem involving listing availability and date ranges. The problem looked deceptively manageable until you actually had to beat the naive approach.

Given N Airbnb listings each with a set of available days, and a requested date range [L, R], return all ordered pairs of distinct listings (X, Y) such that there's some split day s where X covers [L, s] continuously and Y covers [s+1, R] continuously. The naive O(N^2 * R) solution isn't acceptable. Describe a better algorithm with data structures, complexity analysis, and handling for sparse availability and large ranges.

“I started with the brute force just to make sure I understood the problem, then got stuck for a bit on how to actually precompute the useful stuff.”

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Airbnb·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026

Airbnb software engineer interview that leaned way more into product strategy territory than I expected. One big open-ended question about engagement that basically asked you to run a mini product review on the spot.

A consumer app has flat monthly active users. Walk through how you'd identify and prioritize opportunities to improve engagement, including defining a target segment, diagnosing pain points, choosing metrics, generating solutions, and laying out a 90-day roadmap.

“This was a lot to hold in your head at once.”

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Airbnb·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Airbnb system design round focused entirely on building a low-latency analytics platform. The whole thing was a deep technical conversation about stream processing, and it went longer than I expected with a lot of follow-up drilling.

Design a data analytics system where the primary requirement is low query latency over recent data. Walk through your high-level architecture and explain why batch processing frameworks fall short here.
Walk through the stream processing path in detail: how does data get ingested through Kafka, and how does Flink handle real-time aggregations including windowed operations, keyed state, and watermarks?
How do you make this system reliable? Specifically, cover state backends like RocksDB, checkpointing, consumer offset management, and how recovery works after a Flink job failure.
Explain the integration between Kafka and Flink. How do consumer groups work in this context, what are the tradeoffs between exactly-once and at-least-once delivery, and how does backpressure propagate?
How would you design the serving layer to support interactive analytical queries on top of the streaming data you've been describing?

“I started with Spark out of habit and then had to walk it back, which was a little embarrassing.”

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Airbnb·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Airbnb system design round, single big question about building a waitlist API for high-demand listings. Dense problem with a lot of moving parts and I'm not sure I covered everything they wanted.

Design a waitlist API for high-demand listings. Users can join a per-listing, per-date waitlist when their desired date is unavailable, and get notified when inventory opens up. Cover the endpoints, data models, storage, fairness/ordering, notifications, concurrency handling, and scalability.

“This question ate the whole session.”

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