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Scale AI interview notes, page 5 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·Hiring Manager Screen

May 2026
Scale AI MLE screen, basically one question about fit and motivation. Pretty short post to work with so not much to go on.
  • Why do you want to work at Scale AI specifically? What draws you to the company's products, mission, or engineering culture?

“Pretty standard 'why us' question but Scale is specific enough that vague answers probably don't land well.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale.ai MLE interview, part 2 of what sounds like a multi-part coding round. The task was extending a task scheduling structure to support subtasks, which seems straightforward until you actually try to keep the data model consistent.
  • Given a list of task objects with fields like id, deadline, and subtasks (as a list of referenced ids), extend the data model to include a 'consumed' field that tracks subtask usage.

“The structure they gave looked like [{"id":"1"}, {"deadline":2}, {"subtasks":["2","3"]}] and you had to figure out where 'consumed' fits and what it even means in context.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale.ai ML engineer interview that went deep on RL alignment methods. One meaty question covering PPO, DPO, and GRPO back to back, with follow-ups on when you'd actually pick each in practice. Felt more like a whiteboard seminar than a standard interview.
  • Compare PPO, DPO, and GRPO as methods for aligning LLMs: walk through the objective each one optimizes, what kind of reward or preference data it needs, whether it requires a separate reward model or value function, the compute and memory tradeoffs, and the practical strengths and limitations of each. Then explain when you'd choose one over the others.

“This was basically the whole interview packed into one question.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Three-part coding interview for an ML Engineer role at Scale.ai. Finished all parts in an hour and passed every test, but the interviewer was basically silent the whole time which made it feel like talking into a void.
  • Implement an updateDeadline function that checks if a task has already been consumed before updating its deadline, returning null if so.
  • What is the time complexity of your solution?

“This was part 3 of a multi-part coding problem.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale.ai ML engineer screen, one question but it was a deep one. Basically a full project debrief where they wanted everything: why it mattered, what you personally did, the calls you made, what broke, and what you'd tell your past self.
  • Walk me through a current project from start to finish: why it existed, what you specifically contributed, the technical decisions you made and why, the results, and what you learned.

“This question sounds like a warmup but it absolutely is not.”

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Scale AI·Software Engineer·Technical Phone Screen

Jun 2026
Scale AI SWE interview with a data engineering problem that looked like a SQL question but turned into more of an algorithms exercise once you got into the merging logic. Pretty clean problem overall, though the edge cases kept me on my toes.
  • You have two tables: one with party records including start and end timestamps, and one mapping each party to a neighborhood, city, and state. For each neighborhood, merge overlapping or touching time intervals into maximal continuous blocks and return the results sorted by state, city, neighborhood, then block start time.

“The join part was fine, group by neighborhood and pull all the intervals together.”

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Scale AI·Software Engineer·Take-home Assignment

Jun 2026
Scale.ai SWE take-home where you're handed a broken pipeline and told to fix it. Pretty hands-on debugging exercise, nothing abstract about it.
  • You're given a project-assignment pipeline where a record-filter step is using the wrong field key, causing unrelated records to slip through. Trace the data flow, fix the bug, write a regression test, and confirm downstream logic only sees the correct rows.

“The actual bug was pretty small once you found it, a single wrong field name in the filter.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Standard behavioral screen for a software engineer role at Scale.ai. Two questions, nothing technical, pretty low-key overall but the strengths/weaknesses one always makes me squirm a little.
  • Tell me about a side project you've worked on. What motivated you to build it, what did you actually make, and what did you take away from it?
  • What are your biggest strengths and weaknesses? Walk me through a real example for each, and explain what you're actively doing to get better on the weakness side.

“I had an answer ready for this but rambled more than I wanted to.”

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Scale AI·Software Engineer·Technical Phone Screen

Jun 2026
Scale AI SWE coding round, one problem the whole session. It was a task scheduling design question that looked like a simple priority queue thing until the dependency constraints kicked in. Decent problem, made me think harder than I expected.
  • Design and implement a TaskManager class with an AddTasks method and a ConsumeTask method. ConsumeTask should return the executable task with the earliest deadline, where a task is only executable if all its prerequisite tasks have already been consumed. If no executable task exists, return NO_TASK.

“My first instinct was just a min-heap on deadlines and I started coding that up before I actually read the prerequisite part carefully.”

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Scale AI·Software Engineer·Technical Phone Screen

Jun 2026
Scale AI SWE interview with two algorithm-heavy problems back to back. Nothing too wild but the scheduling stuff required some careful thinking about edge cases.
  • Given a set of scheduled intervals, find all the free time gaps between them.
  • Given a list of queries with processing times, schedule them to minimize total processing time.

“Sorting the intervals first was the obvious move but I second-guessed myself for a minute on how to handle overlapping ones before merging.”

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