Airbnb Interview Questions — Page 17

Airbnb·Software Engineer·Technical Phone Screen

Jul 2026

Airbnb SWE interview with a grid traversal problem that looks like a BFS warmup until you realize the state space is way bigger than it seems. Solid problem but I definitely underestimated the complexity going in.

You have an m×n grid with walls, open cells, a start position, up to six keys (a-f), and locked doors (A-F) that require the matching key to pass. Moving one step at a time (up/down/left/right), what is the minimum number of steps to collect all keys? Return -1 if impossible. Walk through your state representation, search strategy, revisit detection, pruning, and time/space complexity.

“I jumped straight to BFS which is right, but my first state representation was just (row, col) and the interviewer let me run with it for a bit before asking what happens when you visit the same cell twice but with different keys collected.”

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Airbnb·Product Manager·Onsite - Behavioral / Leadership

Jul 2026

Behavioral round at Airbnb, one question about navigating friction with a design partner. Short and focused, nothing too wild.

Describe a time you had a difficult working relationship with a design counterpart and how you handled it.

“I had a story ready but it felt a bit thin by the end.”

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Airbnb·Product Manager·Onsite - Product Sense / Strategy

Jul 2026

Airbnb PM interview with a product metrics question focused on a business travel feature. Pretty standard format but the question had some depth to it once you started pulling on the threads.

Airbnb launched a feature aimed at growing its business travel segment. How would you measure whether it's working?

“I went straight to bookings volume and kind of had to walk myself back from that.”

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Airbnb·Software Engineer·Technical Phone Screen

Jul 2026

Airbnb software engineer interview with a meaty dynamic programming problem on job scheduling. The question had a lot of moving parts and the follow-ups kept coming, which made it feel more like a design conversation than a pure coding round.

Given up to 200,000 jobs each with a start time, end time, and reward, find a subset of non-overlapping jobs that maximizes total reward. Return the max reward and the actual job indices selected. Design an O(N log N) solution.
How would you reconstruct the specific set of chosen jobs, not just the maximum reward value?
What are the time and space complexity of your approach, and how does memory usage scale with N up to 200,000?
Could you use a segment tree or coordinate compression to solve this, and what would be the tradeoffs versus the binary search approach?
How would you adapt this solution if jobs arrive in an online streaming fashion rather than all being available upfront?

“The core DP part clicked pretty fast for me.”

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Airbnb·Data Scientist·Technical Phone Screen

Jul 2026

Airbnb data science interview with a pretty gnarly causal inference scenario. No fluff, just one meaty question about estimating impact when you've already blown past the experiment window.

A feature was launched to 100% of users with no control group. How would you estimate the causal impact of that launch, what data would you need, what assumptions are you making, and how do you communicate the uncertainty?

“This one stung a little because I started with pre-post analysis and the interviewer just waited.”

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Airbnb·Software Engineer·Technical Phone Screen

Jul 2026

Two coding problems at Airbnb for a software engineer role. The linked list one was deceptively deep and the round-robin iterator felt more straightforward but had some gotchas around exhausted streams.

Given the heads of two singly linked lists that may each be acyclic or contain a cycle, determine whether the two lists share any node by reference. Follow up: return the actual shared node if one exists.
Implement a round-robin iterator that merges multiple input streams, cycling through them in order and skipping any stream that's been exhausted, without loading all data into memory upfront.

“This one wrecked me a little.”

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Airbnb·Software Engineer·Technical Phone Screen

Jul 2026

Airbnb software engineering interview with a meaty algorithmic problem that required me to think through sorting, DP, and binary search all at once. Tougher than I expected for a single question.

Given a list of jobs each with a start time, end time, and reward value, find a subset of non-overlapping jobs that maximizes total reward. Return both the maximum reward and one valid set of job indices. Target O(n log n) time. Be prepared to explain your algorithm, prove it correct, analyze complexity, and walk through edge cases like zero-length jobs or identical timestamps.

“I knew weighted job scheduling but blanked on how to reconstruct the actual job indices, not just the max value.”

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Airbnb·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Airbnb ML engineer interview with a combinatorics/DP coding problem. The constraint on desired items being small was the key hint, and once I saw it the bitmask approach clicked, but getting there took a minute.

You're given a list of combo meals (each combo is a set of items) with prices, and a target set of items you want. Find the minimum cost subset of combos whose union covers all target items. Extra items in a combo are fine. Return the minimum cost, or -1 if it's impossible.

“Took me a bit to see this wasn't just a greedy problem.”

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

Jul 2026

Airbnb ML system design round, one big question covering the full recommendation pipeline. Pretty dense for a single session but the scope was clear from the start.

Design a large-scale recommendation system end to end, covering candidate generation, ranking, feature engineering, model serving, online vs offline metrics, A/B testing, and how changes to a downstream scoring service can affect live metrics.

“This is basically a full ML systems interview crammed into one question.”

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Airbnb·Data Scientist·Technical Phone Screen

Jul 2026

Airbnb data scientist interview that was basically a full take-home disguised as a technical screen. One massive pandas question covering sessionization, funnel analysis, segment metrics, and statistical lift calculations. A lot to fit into one sitting.

Given a raw events table and a guests table, write idiomatic vectorized Pandas to: sessionize events per guest using a 30-minute inactivity timeout, filter bot traffic, then compute segment-level metrics (unique guests, sessions, session-level and guest-level conversion rates, average sessions-to-first-order, and median time from first page_view to first order_completed) broken out by traffic_source and device. Restrict the analysis to a 7-day window ending 2025-09-01.
Using the same event data, build a de-duplicated guest funnel for page_view, add_to_cart, checkout_start, and order_completed over the 7-day window. Each guest should be counted once at their highest attained step. Report step-through rates and absolute drop-offs.
After filtering segments with fewer than 100 sessions, identify the top 5 segments by lift versus the sitewide guest-level conversion rate. Report both the lift and the 95% Wilson confidence interval for each segment's conversion rate.
How do you handle guests who complete an order_completed event in the window but have no earlier events in the same window? Implement a specific rule: pull their first event from the prior 24 hours if available, otherwise treat them as a direct single-step conversion.
How would you adapt this Pandas pipeline to run at scale in PySpark? What are the key translation points?

“The sessionization part is where I spent most of my mental energy.”

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