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Airbnb interview notes, page 1 of 19

Large Enterprises

Most of these Airbnb write-ups come from Software Engineers (141), then Machine Learning Engineers (19) and Data Scientists (12). The most reported rounds are the technical phone screen (85), the system design round (47), the behavioral / leadership round (20) and the product sense round (13).

187 interview notes · updated Oct 2026

Airbnb·Data Scientist·Technical Phone Screen

May 2023
Airbnb data scientist interview that leaned heavily on applied SQL and Python work, basically building a modeling pipeline from scratch using a user activity table. The questions weren't purely theoretical, they wanted you to actually write code and interpret results, which I wasn't fully prepared for.
  • Given a table of daily user activity with clicks and purchases per variant, write SQL to aggregate this data into a feature set suitable for modeling.
  • Using Python, fit a linear or logistic regression on the aggregated features and walk through how you'd interpret the model coefficients.

“I knew window functions were the move here but I second-guessed myself on whether to use a rolling average or just daily aggregates.”

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Airbnb·Software Engineer·Onsite - Multi Round

Jul 2024
Went through Airbnb's full Staff+ loop after a recruiter reached out on LinkedIn, covering system design, a behavioral deep-dive, coding, and a surprisingly personal core values round. The process was well-organized with a candidate portal for scheduling, and I came out of it feeling good but uncertain about whether I'd land at the level I was hoping for.
  • Walk me through your work experience and the areas of engineering you prefer to work in.
  • Design a large-scale distributed system, including how you'd handle scaling, edge cases, and CAP theorem tradeoffs.
  • Tell me about two large-scale, long-running projects you've led or been deeply involved in.
  • Solve a modified version of a well-known coding problem, then optimize it and discuss how you'd deploy it in production.
  • Behavioral interview focused on Airbnb's core values, including personal and professional situations that reflect those values.

“This was the hiring manager screen and it felt more like a calibration call than a real interview.”

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Airbnb·Data Scientist·Technical Phone Screen

May 2025
Airbnb data science interview with a brutally hard causal inference question about a global launch with no holdout. One question, no warmup, just straight into the deep end.
  • A product feature was shipped globally on a single date with no control group or holdout. How would you design a plan to estimate its causal effect on weekly revenue and 7-day retention? Walk through at least two independent identification strategies, covering assumptions, data needs, diagnostics, uncertainty quantification, how you'd bound effects if assumptions break, how you'd handle confounders like seasonality or concurrent campaigns, and how you'd ultimately reconcile conflicting estimates into a ship/rollback/iterate recommendation.

“This wrecked me a little.”

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Airbnb·Software Engineer·Technical Phone Screen

Apr 2026
Airbnb software engineering interview with a string manipulation problem that sounds deceptively simple but opens up into a pretty meaty discussion. The core task is straightforward but the follow-ups on overlapping matches and efficient multi-pattern search are where it gets interesting.
  • Given a review string and a map of substrings to tags, replace every occurrence of each substring (case-insensitive) with a formatted token like [tag]{matchedText}, while preserving the original casing of the matched text in the output.

“The basic version is manageable.”

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Airbnb·Software Engineer·Technical Phone Screen

Apr 2026
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.
  • 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.

“The singleton case tripped me up at first.”

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Airbnb·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026
ML system design round at Airbnb for an MLE role. The question was meaty and covered a lot of ground, probably the most open-ended design problem I've faced in an interview.
  • Design an ML system to identify high-quality listings on Airbnb so they can be ranked higher in search and low-quality ones can be flagged or coached.

“The first thing I fumbled was the definition of 'quality' itself.”

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Airbnb·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Did a behavioral round at Airbnb for a software engineer role, focused entirely on one project. Felt like the stronger round compared to others. The prep is pretty manageable if you nail the structure: context, goal, impact, and how work was divided.
  • Walk me through a specific project you worked on, including the background, your goal, how work was divided, and what the impact was.

“The whole round was basically this one question with follow-ups woven in.”

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Airbnb·Software Engineer·Technical Phone Screen

Apr 2026
Airbnb coding round focused on building an in-memory key-value store from scratch. The question had a few layers to it and the follow-ups kept coming, so it was more involved than I expected going in.
  • Design and implement an in-memory key-value store supporting get, set, and delete operations. Be prepared to discuss API design choices, underlying data structures, edge cases, and extend the design with TTL or snapshot/restore semantics.

“Started with a plain hashmap and felt good about it, then they asked about TTL and I kind of fumbled the expiry logic.”

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Airbnb·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a SWE role at Airbnb and got hit with a mission-alignment question early on. Pretty short on details to share but it left an impression.
  • Which part of Airbnb's mission resonates most with you, and why?

“Blanked for a second because I hadn't really thought past the surface level answer about travel and belonging.”

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Airbnb·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a PM role at Airbnb and got hit with a classic growth question about increasing bookings. Pretty open-ended, which sounds easy until you're actually sitting there trying to not ramble for ten minutes.
  • As a PM at Airbnb, what would you do to increase bookings?

“I went straight to supply-demand levers without grounding it in any user segment first, which I think was a mistake.”

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