Scale AI Machine Learning Engineer interview questions

30 Machine Learning Engineers have shared notes from their Scale AI interviews, with the questions they were asked in each round. That is 38% of the 79 Scale AI interview notes on Screna AI.

30 Machine Learning Engineer interview notes

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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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·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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale.ai ML Engineer interview had a 45-minute informal presentation round where you pick your own technical topic, usually a past project, and walk through the problem, approach, results, and trade-offs before fielding questions from the panel.
  • Give a 45-minute informal presentation on a technical topic of your choice, covering the problem you tackled, your approach, the results, and the trade-offs involved. Be prepared for follow-up questions.

“Picking the right project matters more than I expected.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Scale AI, basically one question about NLP experience and that was the bulk of it.
  • Walk me through your background and hands-on work with natural language processing.

“Pretty open-ended so I just started rambling about tokenization and a text classification project I'd done.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale AI ML engineer screen, just one question about computer vision pipelines. Pretty standard stuff but I fumbled the structure a bit under pressure.
  • Walk me through what a typical computer vision pipeline looks like end to end.

“I started with data collection and kind of rambled my way through preprocessing, augmentation, model training, evaluation, and deployment without really tying it together.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Scale.ai ML engineer coding round, done on Google Colab with some starter code already provided. Just one implementation task but it was trickier than it looked at first glance.
  • Using only NumPy, implement top-p (nucleus) sampling: apply softmax to a logit vector, sort by probability descending, find the smallest prefix whose cumulative probability meets or exceeds a threshold p, zero out everything outside that prefix, renormalize, and sample one token.

“The starter code had softmax and test cases already written, which honestly helped a lot.”

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

Jun 2026
Behavioral round at Scale.ai for an ML Engineer role, basically one big conflict question that took up most of the time.
  • Tell me about a significant disagreement you had with your manager. What was it about, how did you handle it, and what came out of it? What would you do differently?

“This one went longer than I expected.”

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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·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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