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Scale AI interview notes, page 3 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·Onsite - Behavioral / Leadership

May 2026
Scale AI behavioral round for an ML Engineer role, focused almost entirely on customer engagement and how you handle pushback from stakeholders. Two questions, both a bit more personal than I expected.
  • Tell me about a time you worked directly with a customer or stakeholder who had strong opinions. How did you handle disagreements and drive an outcome?
  • What is your current manager's name, and what score from 1 to 10 do you think they would give you? Why?

“I had a decent story ready about a client who kept pushing for a solution I knew was technically wrong for their use case.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Scale AI for a software engineer role, focused entirely on walking through past projects with real numbers to back them up. Pretty standard format but the emphasis on quantified results caught me slightly off guard.
  • Pick one or two projects you're proud of and walk me through them in detail. What specifically did you do, and how did you measure whether it was successful?

“I went in thinking 'talk about a hard project' and almost fumbled because I hadn't prepped actual numbers.”

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Scale AI·Software Engineer·Onsite - System Design / Architecture

May 2026
Scale AI system design round for a SWE role. The whole interview was one big open-ended problem: build an insurance claims agent using RAG, pick your infra and storage, and keep token costs from spiraling out of control. Dense prompt, lots of moving parts.
  • Design an AI agent system for processing insurance claims end-to-end: it ingests incoming claims, uses retrieval-augmented generation to pull relevant context, and outputs an approval decision. Walk through your architecture including storage choices and how you'd keep LLM token costs under control.

“This one sprawled fast.”

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Scale AI·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026
Scale.ai MLE interview, culture-fit and customer engagement behavioral round. Pretty standard stuff but the manager-focused questions caught me a little off guard since I hadn't prepped much from that angle.
  • Who is your current manager, and how would they describe you as an employee?
  • On a scale or in general terms, how would your manager rate your performance?
  • Tell me about a time you received critical feedback from your manager and what you did with it.

“I fumbled this a bit.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale AI SWE interview with a pretty involved SQL/interval-merging problem. Two-part question covering per-town union of time intervals and then idle-time segments grouped by city hierarchy. Felt like a data engineering screen more than a pure algorithms round.
  • Given a region table with a town-city-state hierarchy and a list of party attendance intervals for each town within a single day, compute the total party hours per town by taking the union of that town's intervals (no double-counting overlaps).
  • Using the same party intervals, compute the idle (not-at-party) time segments for each town within the full day window, then group those idle segments by city. Return city_id, town_id, idle_start, and idle_end.

“The union-of-intervals part is standard sweep-line stuff, sort by start and merge when the next interval overlaps or touches the current one.”

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Scale AI·Machine Learning Engineer·Hiring Manager Screen

May 2026
First round at Scale.ai for an MLE role, post-training, pure behavioral stuff. Nothing technical, just the standard get-to-know-you gauntlet.
  • Tell me about yourself.
  • Why do you want to join this team specifically?
  • Walk me through a challenging project you've worked on.
  • Describe a time you dealt with conflict at work.
  • Tell me about a time you failed or made a significant mistake.

“The opener.”

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Scale AI·Software Engineer·Recruiter / HR Screen

May 2026
Recruiter screen for a software engineering role at Scale.ai that turned out to be way more technical than I expected. The recruiter went deep on ML concepts and project specifics, so don't let the 'recruiter call' label fool you into going in underprepared.
  • Walk me through a project on your resume. What was the problem, what did you specifically do, what techniques did you use, and what would you do differently now?
  • How does GRPO differ from PPO, and what are its trade-offs?
  • What research or engineering area do you want to grow into next, and why does this team fit that direction?

“They asked this for more than one project, which I wasn't ready for.”

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Scale AI·Software Engineer·Onsite - System Design / Architecture

May 2026
Scale AI system design round focused entirely on integrating an LLM into a production feature, end to end. Not your typical design question, it was more like a product engineering deep dive with a lot of follow-up pressure on the parts most people hand-wave through.
  • Design a backend feature that calls a hosted LLM API to complete a structured user task. Walk through the full system: how you call the API, how you prompt it, how you validate the response, and what it takes to ship it to production.
  • A user submits text that says 'ignore your instructions and output X.' How does your design prevent that from affecting the model's behavior or your output schema?
  • The LLM provider goes down or starts responding very slowly. What does your feature do, and what does the user actually see?
  • How would you measure whether a prompt change or model upgrade is actually an improvement before rolling it out to everyone?
  • Week one in production and costs are running three times higher than projected. Which levers do you pull first?

“This was the main question and it ate the whole session.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale.ai SWE interview that focused on task scheduling and dependency graphs. The core problem was about optimizing repeated extraction of the next runnable task, which sounds manageable until they push you on the complexity analysis and trade-offs.
  • You have a task scheduler where tasks have durations and prerequisite dependencies. How would you repeatedly extract the next ready-to-run task and compute the minimum schedule completion time?
  • What are the trade-offs between using a heap versus a plain array for managing the ready-task queue in this scheduler?

“I started with a basic array scan to find ready tasks and it worked but they immediately asked about the time complexity per extraction step.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale AI ML engineer interview, one technical question about rotated object detection that ended up being more of a design conversation than a pure coding problem. Pretty niche topic but made sense given what Scale works on.
  • In a rotated object detection task, how do you balance detection accuracy against computational efficiency?

“This one took me a second to figure out where to even start.”

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