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Scale AI interview notes, page 2 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 - Coding / Algorithms

Apr 2026
Scale.ai SWE coding round with a matrix simulation problem. The rules were different from the classic version but the code structure ended up being almost identical, which was either a relief or a little anticlimactic depending on how you look at it.
  • Given a 2D matrix where each cell can be in one of several states, apply a set of custom transition rules simultaneously to all cells and return the resulting matrix.

“The problem was basically Game of Life with different rules swapped in.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai ML engineer interview focused on implementing token sampling from scratch, covering the full range of decoding strategies. Pretty implementation-heavy, no fluff.
  • Implement LLM token sampling from logits, supporting greedy (argmax), temperature scaling, top-k filtering, and top-p (nucleus) filtering. Return the sampled next-token id under the chosen strategy.

“More involved than it looks on the surface.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Scale.ai ML engineer technical screen focused pretty heavily on regularization fundamentals for deep nets and LLMs. One question but it went deep fast, covering math, implementation details, and modern training practices. Felt like they wanted to see if you actually understood the tradeoffs or just memorized definitions.
  • Explain dropout and L1/L2 regularization for deep networks and LLMs: cover the math, why each reduces overfitting, how they behave differently at train vs inference time, typical hyperparameter ranges, tradeoffs between them, and how they interact with things like LayerNorm or AdamW weight decay. Also touch on why dropout is often turned off during modern LLM pretraining.

“This question looked like one question but was basically five questions stacked on top of each other.”

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Scale AI·Software Engineer·Technical Phone Screen

Apr 2026
Scale.ai SWE interview with a meaty scheduling problem that goes well beyond a basic topological sort. The question layers in parallel workers, dependency graphs, and a specific dispatching heuristic, so if you walk in expecting a clean LeetCode medium you'll be caught flat-footed.
  • Given a set of tasks with durations, a DAG of dependencies between them, and K parallel workers, implement a scheduler that returns the minimum time to complete all tasks. Use longest-processing-time-first when multiple tasks are ready and a worker is free.
  • How would you detect a cycle in the dependency graph, and what should the scheduler do if one is found?
  • Implement the ready_tasks function that, given the current scheduler state, returns all task IDs whose dependencies are fully finished and that are not currently running or already done.

“This one took me a while to even decompose properly.”

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Scale AI·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Scale AI PM interview, one question about how you'd think through compensation for data labeling workers. Pretty niche but makes sense given what the company does.
  • How would you design a pay structure for data labeling teams?

“I went in thinking this was a standard pricing question and it really isn't.”

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

Apr 2026
Behavioral round for an ML Engineer role at Scale.ai. Just one question but it was the kind that makes you squirm a little if you haven't actually thought it through before.
  • What is a personal trait that doesn't serve you well at work? Walk through why you think you have it, how it has affected your work or relationships with teammates, and what you've actually done to address it. Give a concrete example.

“This one is sneaky because it sounds like a standard weakness question but it's asking for a lot more.”

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Scale AI·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Scale.ai behavioral round for a software engineer role, pretty much one big question about a hard project you've worked on. Not a lot of back-and-forth from what I could tell, just dig in and talk.
  • Tell me about a project you found particularly challenging. What made it hard, how did you handle it, and what came out of it?

“I picked a project that was messy on multiple fronts: unclear requirements up front, a few stakeholders who disagreed on scope, and some real technical debt we had to work around mid-way through.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale.ai ML engineer coding round done entirely on Google Colab, no IDE, just NumPy and a starter file with some softmax scaffolding already in place. Two-part attention implementation problem, single-head then multi-head. No LLM allowed, which they apparently enforce somehow.
  • Implement scaled dot-product attention from scratch using only NumPy, given Q, K, and V matrices. Include support for an optional additive mask applied before the softmax.
  • Extend your single-head attention to full multi-head attention: split the embedding dimension across h heads, project Q/K/V separately per head, run attention in parallel, then concatenate and apply an output projection.

“The formula itself isn't the hard part, it's remembering to divide by sqrt(d_k) before softmax and not after.”

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Scale AI·Software Engineer·Technical Phone Screen

May 2026
Scale.ai SWE interview that was basically one meaty coding/design question about extending a task scheduling system with dependency tracking. Not a typical LeetCode grind, more of a design-meets-implementation hybrid that caught me a bit flat-footed.
  • You have a task processor that consumes tasks by earliest deadline using a min-heap. Now each task can have subtask dependencies (other task IDs that must be consumed first). Modify ConsumeTask() to respect these dependencies while still breaking ties by deadline. Also handle cycle detection.

“I started with the heap and worked outward, which was probably the right instinct.”

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Scale AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Scale AI ML Engineer interview focused heavily on adversarial robustness evaluation design, specifically around universal adversarial prompts on small language models like GPT-2. The whole thing felt more like a research methodology discussion than a typical coding screen, which I wasn't fully expecting.
  • How would you design a controlled, offline evaluation framework to assess robustness against universal adversarial prompts using a small model like GPT-2, without creating anything that could be misused?
  • How would your evaluation approach change if you only had black-box API access to the model, with no access to gradients or logits?
  • What steps would you take to prevent the universal adversarial string from overfitting to your training prompt set?
  • What baselines would you include to make the robustness evaluation credible, and what artifacts would you withhold from any public report?

“This was the main question and it took up most of the interview.”

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