Glean Coding Interview Questions
25 Glean coding interview problems with full optimal solutions — 8 easy, 12 medium, 5 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Glean interviewer values, and a FAQ section.
Showing 13 problems of 25
- #1easyvery frequently asked
1. Two Sum
Glean uses this as a warm-up to test whether candidates think in hash maps first — the same O(1) lookup pattern that powers their inverted-index search engine at the core.
- #3mediumvery frequently asked
3. Longest Substring Without Repeating Characters
Glean asks this to test sliding-window fluency — the same technique used in tokenizing and windowing text streams for indexing, where you need to identify the longest unique n-gram span without repetition.
- #4hardsometimes asked
4. Median of Two Sorted Arrays
Glean tests this to verify that candidates can reduce a tricky problem to binary search on the partition point — the same reasoning behind efficiently finding the rank-based cutoff in a dual-index search system without merging both indexes.
- #23hardvery frequently asked
23. Merge K Sorted Lists
Merge K Sorted Lists is a cornerstone Glean interview problem — it directly models merging ranked result sets from K index shards into one ordered output stream, a core operation in any distributed search engine.
- #42hardsometimes asked
42. Trapping Rain Water
Glean asks this hard-tier problem to test whether candidates can derive an O(1)-space two-pointer solution from an O(n)-space prefix/suffix scan — the same optimization mindset that matters in large-scale index traversal where memory allocation is expensive.
- #70easysometimes asked
70. Climbing Stairs
Glean uses this to probe dynamic programming intuition — recognizing that the answer is just Fibonacci reveals whether a candidate spots optimal substructure without prompting, a skill that translates directly to ranking-function design.
- #127hardsometimes asked
127. Word Ladder
Glean uses Word Ladder to test BFS on an implicit graph — a critical skill when traversing semantic neighborhoods in a word-embedding space or computing edit-distance hops between query terms for query expansion.
- #139mediumvery frequently asked
139. Word Break
Glean asks Word Break because query segmentation — splitting a raw search string like 'enterpriseaichat' into 'enterprise ai chat' — is a real pipeline step in their search engine, and the DP solution maps directly to it.
- #146mediumvery frequently asked
146. LRU Cache
Glean uses LRU Cache to test data structure composition — the same hash map + doubly-linked list pattern that sits at the heart of their real-time document caching layer, where recently accessed enterprise content must be served with sub-millisecond latency.
- #208mediumvery frequently asked
208. Implement Trie (Prefix Tree)
Glean is an enterprise search company — Tries are the backbone of autocomplete and prefix-lookup in their search bar. Expect this to come up and expect deep follow-up questions about real-world trie extensions.
- #217easysometimes asked
217. Contains Duplicate
Glean uses this as a fast filter to test whether candidates reach for a set before a nested loop — the exact reasoning behind deduplication in search indexing pipelines.
- #347mediumvery frequently asked
347. Top K Frequent Elements
Glean loves this problem because top-K selection is literally how search result ranking works — retrieving the K most relevant documents from a frequency-weighted index without sorting all candidates.
- #642hardvery frequently asked
642. Design Search Autocomplete System
This is arguably the most Glean-relevant problem that exists — it asks you to build the autocomplete system that is Glean's core product differentiator, combining a Trie with a frequency-ranked top-K retrieval engine.