In this episode of Whiteboard Confidential, an aspiring ML engineer with no formal industry experience impresses a Meta interviewer by tackling a complex system design question: how would you detect fraudulent or scam content on Facebook?

Despite never having held an ML job, the candidate delivers a calm,  clear, and deeply thoughtful design that rivals what you’d expect from  an IC5–IC6 engineer. From feature pipelines and model architecture to  deployment strategy and label bias, this interview is packed with insight. The feedback section is especially valuable, touching on how to ask better clarifying questions, start simpler, and negotiate an offer—even in a tough market.

A must-watch for anyone preparing for ML system design interviews at top tech companies.

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See the interviewer’s feedback and transcript here: 

https://start.interviewing.io/showcase/KQwiaFnBEwAL

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https://interviewing.io/mocks?company=meta

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Timestamps:

00:00 Introduction and candidate background

00:30 System design prompt: ML model to detect scam content

13:00 Architecture walkthrough and feature engineering

36:00 Modeling strategy, focal loss, and label bias

48:00 Interviewer feedback and praise

51:00 Advice on simplifying, asking questions, and tradeoffs

1:07:00 Meta-specific offer negotiation tips and job market talk