Airbnb
Online marketplace for lodging with strong data science and infrastructure.
4 Rounds
~21 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Technical
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medium
In our Learning to Rank (LTR) model for search, we want to optimize for NDCG. Explain the difference between pointwise, pairwise, and listwise approaches. Which would you choose for Airbnb search and why?
#Learning to Rank
#Information Retrieval
#Loss Functions
Machine Learning Engineer
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Technical
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hard
How would you handle position bias in Airbnb's search ranking logs when training a new click-through rate (CTR) prediction model?
#Bias Mitigation
#Click Models
#Feature Engineering
Machine Learning Engineer
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Technical
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medium
We want to build a model to predict the likelihood of a guest canceling a booking. The dataset is highly imbalanced (cancellations are rare). What metrics would you use to evaluate this model, and how would you handle the class imbalance during training?
#Imbalanced Data
#Evaluation Metrics
#Classification
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Always explain your thought process before writing code or drawing architecture.