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Instacart

Grocery delivery marketplace; ML for search, ads ranking and LLM features

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Interview in a few hours? A quick sprint for Instacart

AI in interviews

high confidence

No AI in live interviews unless the interviewer asks for it as part of a skills presentation, and take-homes or assessments must be your own work. The one exception: the senior software engineer process allows and encourages AI in the Karat Next Gen interview only.

Checked 23 Oct 2026Source

Typical interview loop

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

  1. Recruiter screen

    Focus: Background, motivation, fit

    A short call with talent acquisition to confirm your background, motivation and level before any technical step; some ML candidates also report a hiring-manager call early on.

  2. Coding round

    Focus: DSA, clean Python

    A live coding session on data-structure problems where working, readable Python and clear reasoning count for more than tricks; ML candidates describe the difficulty as easy to moderate.

  3. ML concepts round

    Focus: Classic ML fundamentals

    A conversation on classic machine learning: model families, loss and regularisation choices, and how you pick and read evaluation metrics.

  4. ML system design

    Focus: Ranking systems, deployment

    You design an end-to-end ML system, typically a ranking or relevance problem in a marketplace setting, covering data, features, serving and monitoring.

  5. Behavioral and project discussion

    Focus: Ownership, collaboration

    A walk through past projects plus questions on teamwork, conflict and decisions; the recruiter shares the expected topics beforehand and senior hires may get a separate hiring-manager round.

How it weights your plan

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

ML fundamentals+30%
System design+30%
Coding+10%
Transformers-10%

Official prep and resources

Public candidate write-ups

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