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AMD

Instinct GPUs and the open ROCm stack for AI training and inference

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Interview in a few hours? A quick sprint for AMD

AI in interviews

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Checked 20 Oct 2026Source

Typical interview loop

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

  1. Recruiter screen

    Focus: Background and logistics

    A short call on your ML background, frameworks experience, interest in AI hardware, and logistics like location and sponsorship.

  2. Technical interview

    Focus: Coding, ML basics, GPU

    Live coding at roughly LeetCode-medium level plus ML fundamentals and a walk through your projects, sometimes touching GPU computing.

  3. Virtual onsite

    Focus: ML depth, GPU, behavioral

    A day of rounds on ML theory and architecture, coding, ML infrastructure design and GPU optimization on ROCm, ending with a hiring-manager behavioral talk.

How it weights your plan

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

Inference+40%
ML fundamentals+20%
Behavioral+10%
AI coding round-20%
RAG-20%

Official prep and resources

Public candidate write-ups

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