glean software engineer tech phone screen interview experience
Interview Experience
楼主月初面的SDE岗位。当时面完忘记发了。因为是好久没面的第一次coding面试，而且不让用AI，写的磕磕绊绊。最后勉强写完，只跑了简单的测试，没有写很详细的test case。
具体实现细节是一点不记得了，就发出来给大家参考一下。求加大米！
We have a static list of Suggestion objects like [(ngram: string, department: int, score: int),...] that are loaded into our server. Write a function getTopSuggestions(query: string, userDept: int, k: int) that returns the top k valid suggestions by score (high to low), where a valid suggestion is one where "query" is a prefix of "ngram" and department == userDept.
We wan...
Full Details
楼主月初面的SDE岗位。当时面完忘记发了。因为是好久没面的第一次coding面试，而且不让用AI，写的磕磕绊绊。最后勉强写完，只跑了简单的测试，没有写很详细的test case。
具体实现细节是一点不记得了，就发出来给大家参考一下。求加大米！
We have a static list of Suggestion objects like [(ngram: string, department: int, score: int),...] that are loaded into our server. Write a function getTopSuggestions(query: string, userDept: int, k: int) that returns the top k valid suggestions by score (high to low), where a valid suggestion is one where "query" is a prefix of "ngram" and department == userDept.
We want to serve these suggestions as quickly as possible, without blowing up the memory available on our server. Assume our list of Suggestions objects takes up half the memory on our server.
Examples:
[
("software", 1, 5),
("software engineer", 1, 3),
("engineer", 1, 5),
("glean", 2, 12),
("generative ai", 2, 2),
("office", 1, 4)
]
About This Question
This is a candidate experience report from a glean interview for a swe role (newgrad level) during the phone screen round reported in 2026.
It covers the following topics: Strings .
Topics
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About Glean Interview Reports
This question was reported by a candidate who interviewed at Glean. 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 Glean are the higher-signal extractions to take from this report.
For broader preparation context, the Glean 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 Glean reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Glean 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 Glean 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.