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Google DeepMind

Gemini models and frontier research from games to science

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Interview in a few hours? A quick sprint for Google DeepMind

AI in interviews

high confidence

AI is fine for preparing, but don't use any AI tools during live interviews or interview tasks unless you're told otherwise.

Checked 20 Oct 2026Source

Typical interview loop

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

  1. Initial interviews

    Focus: Background & track fit

    A 30-minute recruiter call on your background and experience; the hiring manager may join or hold a separate call.

  2. Skills interviews: coding

    Focus: DSA & working code

    Usually two algorithmic coding rounds in a live environment such as CoderPad, where your code is expected to actually run.

  3. Skills interviews: ML fundamentals

    Focus: ML theory & mathematical intuition

    An oral round on maths, statistics and core ML where you explain the intuition behind each answer.

  4. ML design

    Focus: ML system design

    Designing an end-to-end ML solution (data, model choice, training, deployment) and justifying choices from first principles.

  5. Final interviews

    Focus: Team fit & mission

    Meetings with team leads and leadership, including your likely manager, covering past work, open-ended ML problems and motivation.

How it weights your plan

Selecting Google DeepMind when you build a plan multiplies each topic’s priority score:

ML fundamentals+50%
Coding+30%
System design+20%
AI coding round-20%

Official prep and resources

Public candidate write-ups

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