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Scale AI

Training data, RLHF and model evaluation for frontier AI labs

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Interview in a few hours? A quick sprint for Scale AI

AI in interviews

low confidence

No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.

Checked 20 Oct 2026Source

Typical interview loop

How Scale AI usually runs it for applied AI and LLM engineer roles.

  1. Recruiter screen

    Focus: Background, motivation

    A phone call on your background, motivation and the team; some new-grad applicants skip it and get an assessment first.

  2. Coding screen

    Focus: Fast, clean practical coding

    A timed HackerRank assessment and/or an hour of live coding on practical problems that get extended as you go, where speed matters.

  3. ML take-home (ML roles)

    Focus: Applied ML, data handling

    ML candidates are reported to get a computer-vision or NLP take-home challenge that they later present.

  4. Onsite technical rounds

    Focus: AI system design, debugging

    Back-to-back virtual rounds that lean AI-specific, such as designing an LLM-backed backend and debugging an unfamiliar codebase that calls a model endpoint.

  5. Credo (values) interview

    Focus: Values, ownership

    A behavioral round structured around Scale's internal credos, using examples from your own projects.

  6. Hiring manager conversation

    Focus: Motivation, role fit

    A fit and background conversation with the hiring manager before the hiring decision.

How it weights your plan

Selecting Scale AI when you build a plan multiplies each topic’s priority score:

Coding+30%
RAG+20%
Behavioral+10%
System design+10%

Official prep and resources

Public candidate write-ups

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