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 12 problems of 25
- #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.
- #15mediumfrequently asked
15. 3Sum
Glean uses 3Sum to evaluate whether candidates can reduce a naive O(n³) problem to O(n²) by sorting and applying two pointers — the same space-time reasoning that shows up in multi-term query intersection optimization.
- #49mediumfrequently asked
49. Group Anagrams
Glean asks this because normalizing and bucketing strings by a canonical key is the foundation of term-normalization in search indexing — the same logic that maps 'ran', 'nar', and 'arn' to a single canonical form for retrieval.
- #56mediumfrequently asked
56. Merge Intervals
Glean uses this to assess interval-sweep reasoning — the same logic behind merging overlapping document time-ranges in activity timelines, or collapsing overlapping token spans in entity recognition post-processing.
- #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.
- #200mediumfrequently asked
200. Number of Islands
Glean uses this to probe graph traversal fluency — BFS and DFS over a 2D grid mirror the connected-component analysis used in clustering semantically related documents into topic islands.
- #207mediumfrequently asked
207. Course Schedule
Glean tests cycle detection in directed graphs here — the same topological ordering problem that arises in dependency resolution when indexing hierarchically structured enterprise knowledge bases.
- #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.
- #238mediumfrequently asked
238. Product of Array Except Self
Glean tests prefix/suffix product reasoning here — the same divide-and-accumulate pattern used in precomputing cumulative document scores across a corpus segment without redundant recalculation.
- #322mediumfrequently asked
322. Coin Change
Glean uses Coin Change to assess unbounded-knapsack DP thinking — a pattern directly analogous to finding the minimum number of query re-expansions needed to cover a target relevance budget.
- #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.