Discuss ML Project Tradeoffs
Company: Airbnb
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
You are interviewing for a senior machine learning role and are asked to discuss a past recommendation or prediction project in depth. Use one concrete project as context and answer the following technical decision-making questions:
1. Why did you choose the modeling approach you used?
2. Why did you not use a more complex model?
3. How would you trade off precision, recall, latency, and infrastructure cost?
4. What would you do if offline metrics and online business metrics disagree?
5. How would you tune a decision threshold for a binary classifier?
6. Compare a Factorization Machine with an embedding-based deep neural network for recommendation or ranking.
Overview: This question evaluates a candidate's skills in model selection, trade-off analysis between precision, recall, latency and infrastructure cost, threshold tuning, reconciliation of offline versus online metrics, and comparative understanding of recommendation models within the Machine Learning domain.
Read the full Airbnb Machine Learning Engineer interview experience this question came from