Airbnb Machine Learning Engineer interview questions

19 Machine Learning Engineers have shared notes from their Airbnb interviews, with the questions they were asked in each round. That is 10% of the 187 Airbnb interview notes on Screna AI.

19 Machine Learning Engineer interview notes

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

Jul 2026
ML system design round at Airbnb for an MLE role. The whole thing was a single deep-dive on building a listing LTV model, and it went way longer and wider than I expected. There's a lot of surface area here.
  • Design an ML system to predict the lifetime value of an Airbnb listing. Walk through your end-to-end approach.
  • How would you define LTV for this problem, and what clarifying questions would you ask before modeling anything?
  • What data sources and features would you use, and how would you engineer them for this model?
  • Many listings are still active and haven't reached the end of their lifetime. How do you handle censored data in your training set?
  • Would you consider a two-stage modeling approach here? What would that look like?
  • How do you handle the cold-start problem for brand new listings with no booking history?
  • What offline and online metrics would you use to evaluate this system, and how do you connect them to business outcomes?
  • How would you deploy this model and how often would you retrain or refresh it?

“This is one of those questions where the scope keeps expanding the more you talk.”

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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·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 - Coding / Algorithms

Jun 2026
Airbnb MLE coding round with a coin change problem that had a float twist. The float precision pitfall is the kind of thing that seems obvious in hindsight but absolutely isn't in the moment.
  • Given a list of coin denominations expressed as floats (e.g. 0.25, 0.5, 1.0), determine whether a target value can be made from those coins, or find the minimum number of coins needed.

“Started doing DP straight on the float values and got maybe halfway through before the interviewer nudged me about precision.”

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

Jun 2026
ML system design interview at Airbnb for an MLE role, basically one long deep-dive into building a search ranking system from scratch. Pretty thorough coverage of the whole stack, from retrieval pipelines to fairness concerns to A/B testing.
  • How would you frame the search ranking problem for Airbnb? Should you use pointwise, pairwise, or listwise learning-to-rank, and what label would you use (booking, click, or something else)?
  • Walk me through the data sources you'd use to train the ranking model. What features come from listings, hosts, users, and the query context itself?
  • Describe the two-stage retrieval and ranking pipeline you'd build. How does the candidate retrieval stage differ from the final ranker?
  • What features would you engineer for the ranker? Think about listing quality, price competitiveness, host responsiveness, and personalization.
  • What model architecture would you choose for the ranker, and why? Compare gradient boosted trees versus a neural ranker.
  • What offline and online metrics would you use to evaluate the ranking system?
  • How would you handle cold-start for new listings and new users who have no historical data?
  • How do you ensure fairness and result dispersion in the ranked list? What happens if the model always surfaces the same top listings?
  • How would you handle online serving given latency constraints, and how would you design an A/B test to validate the new ranker?

“This is where I spent probably too long.”

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

Jun 2026
Round 3 of the Airbnb ML Engineer loop was a deep system design session focused on retrieval and ranking, and it ran long. They clearly care a lot about marketplace ranking experience, so if that's not your background, you'll feel it.
  • Design a user embedding and two-stage semantic retrieval and ranking system to surface listings a user is most likely to book, with the goal of maximizing booking conversion.
  • How do you handle position bias in a click-based training signal for a ranking model?

“This was the whole round basically.”

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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·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Airbnb ML Engineer loop had a values-heavy behavioral segment that I wasn't fully prepared for. Three questions, all tied to mission and personal conviction, and they clearly wanted more than polished talking points.
  • Why do you want to work at Airbnb specifically?
  • In your view, what is one of the most significant problems facing the world right now?
  • What cause do you personally care about, and what have you actually done to support it?

“I had an answer ready but it felt thin the moment I said it out loud.”

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Airbnb·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Airbnb ML engineer coding screen, one question but it had real depth with two follow-ups that caught me off guard. The base problem looked easy and I almost underestimated it.
  • Parse a URL query string into a dictionary, handling keys with no value, repeated keys, percent-encoded characters, and malformed inputs.

“The base case took maybe five minutes and I felt good about it.”

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Airbnb·Machine Learning Engineer·Technical Phone Screen

May 2026
Airbnb ML engineer round with a graph/union-find coding problem. A variant of the classic accounts merge, extended to handle both emails and phone numbers as shared identifiers. Pretty standard for the space but the added contact type wrinkle tripped me up a bit.
  • Given a list of accounts where each account has a name and a list of contacts (both emails and phone numbers), merge all accounts that share any common email or phone number. Return the merged accounts with the union of all contacts.

“My first instinct was to just do it for emails like the standard version, and I literally forgot to handle phone numbers as a separate identifier type for a solid two minutes.”

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