Airbnb interview questions
Big tech · 4 questions reported
- junior 0
- mid 0
- senior 4
These are 4 technical questions candidates reported being asked in Airbnb interviews for AI engineer, ML engineer and applied AI engineer roles, each with a model answer you can say out loud in a minute or two. They cover ml system design and rag: 0 junior, 0 mid-level and 4 senior questions. Every question comes from a public interview write-up that names Airbnb.Updated
Airbnb retrieval-augmented generation questions
Model answer
Index policies as versioned documents with metadata: region, product, user type, effective dates. Extract the case facts from the ticket first, retrieve policies whose metadata matches, then run an applicability check: does each candidate cover this region, date and booking type? The model writes numbered steps, each citing a policy clause, or says none applies.
Log policy versions, retrieved chunks and prompt version with every answer. When an agent reports a wrong instruction, replay it against that exact policy set to see whether retrieval, applicability or generation failed.
Airbnb mL system design questions
Model answer
Candidate retrieval: a two-tower model embeds the user, from search context and booking and click history, and each listing, from attributes, location and engagement. Train it on bookings as positives with in-batch negatives; an ANN index returns a few thousand listings, filtered to the searched market and available dates.
Ranking: a richer model scores those candidates with price, availability and user-listing cross features, predicting booking probability. Cold-start listings rely on content features. Judge offline with NDCG on held-out bookings, then run an A/B test on bookings.
Model answer
Intent is structured: location, dates, guests, filters, plus user history. Rank by predicted booking probability from listing, user and context features.
Logged data reflects past rankings, since people click what was shown first, so correct for position bias: train with position as a feature and fix it to a constant at serving, or weight examples by inverse propensity estimated from randomised slots. New listings get content-based priors and a little exploration traffic. Evaluate offline with NDCG on bookings, then A/B test bookings and cancellations, watching new-host exposure.
Model answer
Define the label first, in a written policy: amenities like cribs and high chairs, hazards like unfenced pools. Labels come from host attributes, guest reviews mentioning children, and trained annotators on a stratified sample, with agreement measured.
Features: amenities, listing text and photos through a multimodal model, review signals. A wrong yes hurts a family more than a missed listing, so tune for high precision and send uncertain listings to review. Evaluate per region, launch as an A/B test on filter use, bookings and complaints, and let hosts dispute labels.
Each question comes from a public interview write-up or video that names Airbnb; the answers are ours. Not affiliated with Airbnb.