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Scale AI interview notes, page 1 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·Technical Phone Screen

Apr 2026
Scale.ai SWE interview that went deeper than I expected. Started with a straightforward scheduler design and then they twisted it into a dependency graph problem mid-question. Felt like two problems in one session.
  • Design a TaskScheduler with an AddTasks method (each task has an id, deadline, and list of subtasks) and a ConsumeTask method that returns the task with the earliest deadline, or 'no task' if none exist.
  • Now extend the scheduler so that each task's subtasks field represents prerequisites. ConsumeTask should only return tasks whose prerequisites have all been consumed already. How do you maintain this efficiently?

“The first part felt fine.”

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

Apr 2026
Scale.ai system design round for a software engineering role. The whole thing centered on extending a task processor to support subtask dependencies, which sounds manageable until you're actually in it trying to juggle DAGs, heaps, and cycle detection at the same time.
  • Extend a base Task Processor so tasks can have subtasks. A parent task should only be processed once all its subtasks are done. Tasks still have deadlines, and among all ready tasks the one with the earliest deadline goes first. Implement add_task() and process_next().

“I spent too long on the happy path and almost forgot to address cycle detection until they nudged me.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai ML Engineer technical screen, pretty deep on transformer internals. The whole session was basically one long implementation question with a lot of follow-up threads pulling off of it.
  • Implement multi-head self-attention from scratch using NumPy or PyTorch. Given Q, K, V projections and a number of heads, compute scaled dot-product attention per head, concatenate the results, and pass through an output projection. Also explain causal and padding masking, and why you scale by 1/sqrt(d_k).

“I started with the math and worked forward, splitting Q/K/V into heads by reshaping, running softmax(QK^T / sqrt(d_k))V per head, then cat and project.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai ML engineer interview that went deep on post-training fundamentals. One meaty technical question on supervised fine-tuning that covered a lot of ground, felt more like a whiteboard lecture than a back-and-forth.
  • Walk me through supervised fine-tuning for large language models, including the training objective, how you prepare and format data, what hyperparameters matter, common failure modes, and where it fits in the broader post-training pipeline.

“This question sprawled in a way I wasn't ready for.”

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

Apr 2026
Behavioral round for an ML Engineer role at Scale.ai, basically one big question about a project you're proud of with a bunch of follow-ups baked in. Pretty standard format but the depth they expect is not.
  • Tell me about a project you're most proud of. Walk through the context and goals, what you personally contributed, the technical and non-technical challenges you faced, and what the actual outcome was. Be ready for follow-ups on trade-offs and what you'd change.

“This is one of those questions that feels easy until you're mid-answer and realize you've been talking for four minutes and haven't gotten to the actual impact yet.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai MLE interview, looks like just one coding question the whole round. Heap problem, went fine.
  • Implement a task scheduler given a list of tasks, each with an id (string) and a deadline (integer). Schedule them appropriately.

“Pretty standard heap question once you see what they're asking.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai ML Engineer technical screen focused almost entirely on tokenization internals. One meaty question that spiraled into a bunch of sub-topics I wasn't fully prepped for.
  • Walk me through how tokenization works in modern LLMs, and explain the trade-offs involved in choosing vocabulary size, covering different tokenization schemes, memory and compute costs, attention complexity, and how these considerations shift for multilingual or code-heavy corpora.

“This started feeling manageable and then kept expanding.”

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Scale AI·Software Engineer·Technical Phone Screen

Apr 2026
Scale.ai Forward Deployed Engineer interview, AI coding round done in a Google Colab notebook. The task was filling in chunks of Python code to run LLM evaluations, about an hour total. Pretty practical and hands-on compared to your typical leetcode grind.
  • Complete the missing sections of a Python notebook to implement LLM evaluation logic.

“Worked through it in Colab, filling in the blanks across a few code cells.”

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Scale AI·Software Engineer·Technical Phone Screen

Apr 2026
Scale.ai SWE interview with a multi-part coding problem involving a task scheduler. The third part asked you to add an updateDeadline method with some concurrency-aware logic.
  • Add an updateDeadline method to a task scheduler. If a task hasn't been processed yet, allow its deadline to be updated. In the consume logic, verify that the deadline and task ID still match before processing.

“The method itself wasn't a ton of code but the tricky part was the consume side check.”

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Scale AI·Machine Learning Engineer·Hiring Manager Screen

Apr 2026
Hiring manager round at Scale.ai for an MLE role, roughly 30 minutes. Most of it was a deep dive into a project I'd worked on, then they flipped it and let me ask questions for the back half.
  • Walk me through your most impactful project. What specifically did you contribute, what technical decisions did you own, and what were the outcomes?
  • What questions do you have for me about the team, the work, and what success looks like here?

“This was basically the whole interview.”

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