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Glean interview notes, page 1 of 3

Mid-sized

Most of these Glean write-ups come from Software Engineers (15), then Machine Learning Engineers (7) and Data Scientists (4). The most reported rounds are the technical phone screen (23), the system design round (2), the behavioral / leadership round (1) and the take-home assignment (1).

27 interview notes · updated Jul 2026

Glean·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Glean ML engineer interview with a graph traversal problem disguised as a transit scheduling puzzle. Clean problem statement but the follow-ups are where it gets interesting.
  • Given a train schedule where each train is a list of stations (index = arrival time), implement a function that determines whether a passenger can travel from a start station to an end station. Passengers can transfer between trains at shared stations even if they arrive at different times, since they can wait.

“The waiting mechanic is the part that trips people up.”

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Glean·Software Engineer·Technical Phone Screen

Apr 2026
Glean SWE interview with a classic two-sorted-arrays problem, but they pushed pretty hard on the optimal solution and edge cases. Not a warmup question by any stretch.
  • Given two sorted arrays of integers, find the k-th largest element across both arrays combined. What approaches can you think of, and what are the tradeoffs between them?

“I started with the merge-from-the-back approach, which is fine but they clearly wanted more.”

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Glean·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed at Glean for a software engineer role and got hit with Word Search as the coding problem. Pretty standard grid traversal territory but still nerve-wracking when you're on the spot.
  • Given a 2D board of characters and a target word, determine whether the word exists in the grid by traversing adjacent cells (up, down, left, right) without reusing the same cell.

“Classic backtracking problem and I knew it, but I still fumbled the visited-cell tracking for a second.”

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Glean·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a software engineer role at Glean and got a 2048 implementation problem, which I thought was kind of a fun twist on the usual grid stuff. The edge cases around merging are where it gets tricky and where I spent most of my time fumbling.
  • Implement the tilt/swipe operation for a 2048 board. Given a 4x4 grid and a direction, shift and merge tiles correctly, respecting the rule that each tile can only merge once per move.

“I knew 2048 as a game but had never thought about coding the move logic.”

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Glean·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an MLE role at Glean and got a graph/search problem that looked like a scheduling puzzle. Two parts, progressively harder, and the second part tripped me up more than I expected.
  • You're given a set of train itineraries, each as a sequence of (station, time) stops. Given a start station, start time, and destination, find the earliest possible arrival time at the destination.
  • Extend your solution to reconstruct and print the full route: every station visited, the time, which train was taken, any waits, and any transfers between trains.

“My first instinct was Dijkstra but then I realized the waiting mechanic made it weird.”

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Glean·Software Engineer·Technical Phone Screen

Apr 2026
Glean SWE interview that was essentially a two-part grid search problem. Part one was straightforward backtracking, but part two pushed into trie territory and that's where things got interesting. The follow-up questions after the main problem were genuinely tricky and I wasn't fully prepared for all of them.
  • Given a 2D grid of letters, determine whether a target word can be formed by following a path of horizontally or vertically adjacent cells, where no cell can be used more than once in a single path.
  • Now given a large list of words (up to 30,000), return all distinct words from the list that can be found on the same board. Running the single-word search once per word is too slow, so how do you optimize?
  • How does pruning matched or childless nodes from the trie change the worst-case versus practical running time for the board search?
  • If the board were very large but the dictionary was tiny, would you still use the trie approach or would you index the board differently?
  • How would you parallelize the board search across many starting cells or board regions, and what shared state would need synchronization?

“Classic backtracking, I knew it.”

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Glean·Frontend Engineer·Technical Phone Screen

May 2026
Interviewed for a frontend role at Glean and got hit with a coding problem straight out of a game jam. The 2048 tilt mechanic sounds like a fun warmup until you're actually trying to handle merges and edge cases under pressure.
  • Implement the tile-sliding and merging logic from 2048. Given a 4x4 board, tiles should slide in a given direction (up, down, left, right), and adjacent tiles with equal values merge into one tile of double the value. Each tile can only merge once per move. Handle all four directions.

“I started with just left-slide since that felt easiest to reason about, got that working, then tried to rotate the board to reuse the same logic for other directions.”

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Glean·Software Engineer·Technical Phone Screen

May 2026
Glean software engineer interview with a classic game implementation problem. Pretty straightforward on the surface but the follow-up about end-game detection is where things get interesting.
  • Implement the 2048 game. Follow-up: how do you detect when the game is over?

“The core implementation felt manageable once I mapped out the tile-shifting logic on a 4x4 grid.”

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Glean·Data Scientist·Technical Phone Screen

May 2026
Interviewed for a DS role at Glean and got hit with a broad product evaluation question that covers basically everything you'd want to study for. No fluff, just a deep dive into how you think about measuring success end to end.
  • How would you evaluate whether a product or newly launched feature is successful, from a data science and product analytics perspective?

“This question sounds broad until you realize they want the whole framework, not just 'pick a metric.' I started with the north-star metric and worked outward, covering acquisition through retention, but I fumbled a bit when they pushed on causal inference.”

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Glean·Software Engineer·Technical Phone Screen

May 2026
Glean SWE interview that included Word Search II as a follow-up problem. Not a lot of context to go on, but it sounds like the coding round leaned into harder graph/backtracking territory.
  • Solve Word Search II: given a board of characters and a list of words, find all words that can be formed by sequentially adjacent cells on the board.

“This was a follow-up, so I was already a bit drained.”

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