System Design Practice

Usually the highest-leverage round to prepare deliberately for, because it's the one most candidates under-practice relative to how heavily it's weighted.

Practice the Framework, Repeatedly

Work through the 9-step ML system design framework on 5-10 different problems, not just once. The framework itself is simple to read — the skill is applying it fluently under time pressure, on a problem you haven't seen before, which only comes from repetition across varied problems (see the case studies for a starting set: recommendations, search, GenAI/RAG, agent systems).

Practice GenAI/LLM System Design Specifically

This is the fastest-growing category of system design question as of 2026, and many candidates who are strong on "classic" ML system design (recommendation, search) haven't drilled the GenAI-specific version: designing a RAG pipeline with explicit metrics and serving architecture, or designing an agent system with cost controls and observability built in from the start (see ML System Design — GenAI Case Studies).

Time Yourself

Most system design rounds run about 45 minutes. Practice pacing: spend real time on problem formulation and metrics up front (rushing this step is the most common mistake — a well-scoped problem with a mediocre model design still shows strong judgment; a vague problem with a clever model design does not), then move through the rest of the framework, leaving time at the end to discuss scaling/monitoring rather than running out of time before reaching it.

What Interviewers Are Actually Evaluating

Not whether you land on the "correct" architecture (there often isn't a single correct one) — but whether you can reason clearly about tradeoffs out loud, ask clarifying questions instead of assuming requirements, and structure an answer instead of free-associating. A structured, average answer beats a brilliant, disorganized one in this round almost every time.

Next: Behavioral — the round that's easiest to under-prepare for and most costly to walk into unprepared.