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Waymo

Driverless robotaxis built on deep learning for perception and planning

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Interview in a few hours? A quick sprint for Waymo

AI in interviews

high confidence

No AI in Waymo interviews: coding and technical assessments are done on your own without AI tools or LLMs unless the instructions say otherwise, and live technical, behavioral and design rounds are AI-free. AI may only be used to polish the wording of your resume.

Checked 22 Oct 2026Source

Typical interview loop

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

  1. Recruiter screen

    Focus: Background, motivation, role fit

    A first call with a sourcer or recruiter about your background and the role; a good moment to ask about the timeline and the rounds ahead.

  2. Technical phone / video screen

    Focus: DSA coding at SWE level

    One or two interviews with a potential peer or manager; for ML engineers this is usually a live data-structures-and-algorithms problem held to the regular software engineer bar.

  3. Onsite: applied coding

    Focus: Problem framing & working code

    A practical coding problem, often framed around a driving-style scenario rather than a textbook puzzle, where clarifying the prompt and reaching working code in time matter.

  4. Onsite: ML coding

    Focus: NumPy fluency, ML implementation

    Hands-on array and data manipulation in NumPy plus implementing a model component from scratch, judged on numerical correctness and fluency.

  5. Onsite: ML system design

    Focus: ML design, data scale, evaluation

    A conversational design of an ML pipeline such as retrieval, ranking or evaluation over large driving datasets, with probing follow-ups.

  6. Onsite: behavioral rounds

    Focus: Ownership, leadership, collaboration

    Two conversations: a deep dive into one high-impact project you led, and one on conflict, influence and mentoring across teams; leveling is decided from these.

How it weights your plan

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

Coding+30%
ML fundamentals+30%
System design+20%
Behavioral+20%

Official prep and resources

Public candidate write-ups

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