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Scale AI interview notes, page 8 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·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Scale.ai system design round for a software engineer role, one big open-ended question about building a task scheduler from scratch. Took up the whole session and went pretty deep into territory I wasn't fully prepared for.
  • Design a task-scheduler service where each task has an id, a deadline, and a list of prerequisite task ids. Clients can add tasks and consume the next runnable task with the earliest deadline. Walk through validation, cycle detection, concurrency, and handling a continuous stream of incoming tasks.

“This question sprawled in a way I didn't anticipate.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale.ai ML Engineer coding round where they drop you into a broken training script and expect you to debug it in under an hour. Four bugs, all small, but reading someone else's ML code under time pressure is a different skill than writing your own.
  • You are given a Python ML training script with 3 to 4 bugs. Each bug can be fixed in about 1 to 2 lines. Run the script, read the errors and outputs, identify each bug, and patch the code so training runs correctly. You have 60 minutes.

“The bugs themselves weren't that hard once I found them.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Scale AI for a software engineering role, just one conflict-resolution question but they clearly cared a lot about how you handle disagreement without blowing things up.
  • Tell me about a time you had a conflict with a teammate, stakeholder, or manager. What caused it, how did you handle it, and what happened in the end?

“I structured my answer as situation, action, result and it felt pretty natural, but I spent too long on the backstory and rushed the actual resolution part.”

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Scale AI·Software Engineer·Technical Phone Screen

Jul 2026
Scale.ai SWE coding round, pretty standard data structures stuff. One question on a task scheduler implementation that was more straightforward than it sounds once you figure out the right structure.
  • Implement a TaskScheduler class with an AddTasks method that accepts a list of tasks (each with an id, deadline, and subtasks field) and a ConsumeTask method that pops and returns the task with the earliest deadline. If the scheduler is empty, return the string 'no task'.

“I went straight to a min-heap keyed on the deadline field and they seemed fine with that.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round at Scale AI for a software engineer role, just one question but they really wanted you to dig into the mechanics of how you learn under pressure, not just the outcome.
  • Tell me about a time you had to pick up something new very fast to keep a project on track. Walk me through how you approached it and what actually happened.

“I structured my answer as situation, action, result and it felt pretty solid in the moment, but looking back I spent way too long on the context and rushed the part about what I actually did to learn.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Scale AI ML Engineer interview that went deep on LLM fundamentals, covering everything from transformer internals to RLHF and distributed training. Technically dense across the board, felt more like a research discussion than a standard coding screen.
  • Walk me through the Transformer architecture and explain how self-attention works, including its computational complexity and why multi-head attention is useful.
  • What's the difference between self-attention and cross-attention, and how does scaled dot-product attention fit in? When would you use each?
  • Define causal decoding and explain how attention masks enforce it during autoregressive generation.
  • Compare greedy decoding, temperature sampling, top-k, nucleus sampling, and beam search. What are the trade-offs in terms of output quality, diversity, and latency?
  • How is reinforcement learning applied to fine-tune large language models? Cover reward modeling, preference data collection, policy optimization approaches, and how you manage training instability.
  • Design an evaluation framework for a large language model. How would you handle automatic metrics, task-based benchmarks, human evaluation, safety testing, data leakage, and statistical significance?
  • What are the key techniques for optimizing LLM training and inference? Cover things like optimizer choices, learning rate schedules, mixed precision, gradient checkpointing, parameter-efficient fine-tuning, and distributed training strategies.

“I knew this cold so it felt fine at first, but they kept pushing on the complexity angle.”

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

Jul 2026
Scale.ai system design round for a Forward Deployed Engineer role, one hour, one question about designing a task quality review system. Pretty open-ended and I wasn't totally sure how deep to go on the ops side versus the technical architecture.
  • Design a task quality review system.

“This fits Scale pretty naturally given what they do, so I expected it, but I still fumbled the scoping a bit early on.”

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Scale AI·Software Engineer·Take-home Assignment

Jul 2026
Scale.ai gave me a take-home style coding exercise where they handed over a multi-file project and asked me to find and fix bugs in it. The setup was more involved than I expected, lots of files, CSV inputs, and you have to actually run the thing and poke around with print statements to find what's broken.
  • You're given a multi-file codebase with CSV inputs. The interviewer walks you through the high-level logic, then you have to read the code, run it, and use print statements to find a bug where an off-by-one error in a headcount capacity check lets one too many people get assigned to a project. Find the file and line, explain the issue, and fix it.

“The bug itself wasn't that hard once I actually ran the code and added some prints around the assignment loop.”

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Scale AI·Machine Learning Engineer·Onsite - Coding / Algorithms

Jul 2026
Did a virtual onsite ML coding round at Scale with two parts: data prep and debugging. Found 2 out of 3 bugs in the debugging section and now I'm just sitting here wondering if that's enough to pass.
  • Given a dataset, perform data preparation tasks as part of an ML coding assessment.
  • Debug a provided ML code snippet and identify all bugs present.

“This part felt more manageable.”

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