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Scale AI interview notes, page 4 of 8

Mid-sized

Most of these Scale AI write-ups come from Software Engineers (48), then Machine Learning Engineers (30) and Product Managers (1). The most reported rounds are the technical phone screen (45), the behavioral / leadership round (12), the system design round (10) and the hiring manager screen (3).

79 interview notes · updated Jul 2026

Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale AI ML engineer interview with a probability/statistics question that felt deceptively simple at first glance.
  • If you have ten points uniformly distributed across the interval [0, 1], what is the distribution of the gap between the 5th and 6th order statistics?

“Took me a second to even parse what they were asking.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a Software Engineer role at Scale AI. Just one question but it was a deep one, they really wanted the full story on a hard project, not just a surface-level summary.
  • Walk me through a project you worked on that was particularly challenging, including your role, what made it hard, what you did, the outcome, and what you'd do differently.

“This is the kind of question that sounds easy until you're in it and realize your story has no real numbers attached.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale AI software engineer interview with a tree algorithm problem. Single coding round focused on graph traversal fundamentals, nothing too wild but the N-ary twist on a classic problem kept me on my toes.
  • Given the root of an N-ary tree and two distinct node references p and q, find their lowest common ancestor using a recursive DFS approach that completes in a single traversal.

“I know LCA for binary trees pretty well but the N-ary version tripped me up for a minute because you can't just go left/right anymore.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale.ai coding round, one part, no dependency stuff to worry about. Pretty clean problem if you know your data structures.
  • Design and implement two functions: one that accepts a list of (taskId, deadline) pairs and adds them to a task queue, and another that processes and returns the task with the smallest deadline.

“Heap was the obvious move here and I went straight for it.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale AI ML Engineer interview focused almost entirely on practical debugging and engineering rigor. Three questions, all technical, all with a time pressure element baked in. Not a vibe check at all.
  • Given messy raw text files with inconsistent formatting, implement a robust parser that outputs structured examples. You need to handle delimiters, quoting and escaping, encoding issues, missing fields, and malformed lines. Also describe how you'd test it.
  • You're given a real ML project codebase covering data loading, preprocessing, training, and evaluation. Find and fix three bugs, which could include things like an off-by-one error in tokenization, train/test data leakage, wrong loss reduction, broken random seeding, or shape mismatches. Walk through your debugging approach.
  • How would you validate all three fixes within a 60-minute window? Cover unit tests, a full end-to-end run, metrics sanity checks, and regression guards.

“This one tripped me up more than I expected.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale.ai coding round for a software engineer role. One problem, simulation-style, not too bad on the surface but there's a bit of nuance in how you track state over time.
  • You're given a list of party events, each with a timestamp string in HH:MM format and a type of either ARRIVAL or DEPARTURE. Compute the attendance count at each hour of the day and return the peak attendance across all hours.

“My first instinct was to overcomplicate this with intervals.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale.ai coding round, part two of what seems like a multi-part problem. The first part was already tricky but this dependency extension is where things get genuinely hard.
  • Extend a task scheduling system so that each task can have subtasks, and a task may only be scheduled for processing once all its subtasks are complete. The smallest-deadline-first ordering still applies.

“The dependency angle is what makes this problem worth thinking about carefully.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale AI SWE coding round. You get a partially built React app and have to finish it. More UI wiring than algorithm work, which honestly threw me off a bit since I came in expecting leetcode.
  • Build a custom React hook that fetches data from a provided API using useEffect with an async function, then transforms the response into a specified format. The hook should expose loading and error states alongside the converted data.
  • Modify an existing UI component to support opening and closing a modal, expanding and collapsing a list, and wiring up several buttons to perform specified actions. All interactions need to be connected to component state and the custom hook.

“The async-inside-useEffect pattern tripped me up for a second.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale AI SWE coding round, one problem the whole session. Matrix simulation with a follow-up asking you to redo it in O(1) space. The problem itself was fine but the space optimization part is where things got interesting.
  • Given an m x n integer matrix representing neurons (0 = firing, >0 = non-firing), update each cell simultaneously based on how many of its 8 neighbors are firing. Rules: a firing cell with exactly 3 firing neighbors becomes 6; a non-firing cell with 0 or 1 firing neighbors loses 2; a non-firing cell with more than 3 firing neighbors loses 1; values can't go below 0; otherwise no change. First implement with a copied matrix, then optimize to O(1) auxiliary space.

“The copy-based version came together pretty quickly.”

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Scale AI·Software Engineer·Take-home Assignment

May 2026
Scale.ai SWE debug round, last of the assign project section. One bug left and it was a loop state issue, the kind that feels obvious in hindsight but takes a second to see when you're under the clock.
  • In a loop where a per-iteration variable (counter, accumulator, or temporary set) is not being reset at the start of each iteration, identify the bug, fix the reset, and verify each iteration runs from a clean state.

“Took me longer than I'd like to admit.”

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