1p3a Experience · May 2026

seeking advice for data science onsite interview at airbnb

Data Science Onsite newgrad

Interview Experience

大家好,楼主最近在面airbnb的DS(ML 岗),有一轮ML practical,地里有人分享过MLE 的ML system design 但是对DS 来说是不是题库是不同的?ML topic跨度太广了 从推荐算法系统到fraud detection 到NLP, 隔行如隔山,很难在短时间内补齐深度和广度。面试日期将近,所以想试试地里有没有airbnb的DS前辈可以给些意见!找工不易,这已经是我最后一个面试,希望好心人能指点迷津!(有偿) 同时在面ML design的朋友也可以联系我分享信息,互相mock,祝大家身体健康,都能找到理想工作! 顺便分享coding轮内容:给3个file(包括run.py, evaluate.py etc.),手动写fit transform,需要run出来,25min是debug, 35min是优化。如果你平时经常写prod code,会很容易,需要复习基础用法。

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About This Question

This is a candidate experience report from a airbnb interview for a data science role (newgrad level) during the onsite round reported in 2026.

It covers the following topics: System Design .

About Airbnb Interview Reports

This question was reported by a candidate who interviewed at Airbnb. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Airbnb are the higher-signal extractions to take from this report.

For broader preparation context, the Airbnb interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Airbnb reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Airbnb Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Airbnb reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.