ML system design
ML system design interview: find the trade-off you cannot defend
Recommenders, RAG, ranking, real-time inference. You explain the design out loud; the follow-ups find the decision you made without being able to justify it.
15 free minutes every week
- About 15 minutes
- Voice-first
- No scores
Coverage
3 of 6 areas checked
- Prompt groundingChecked in this session
- Re-rankingChecked in this session
- Failure triageChecked in this session
- Chunking strategyNot yet tested
- Embedding choiceNot yet tested
- Eval harness designNot yet tested
The diagram is not what fails. The defence is.
Most people can draw a serving architecture. Far fewer can answer why they chose that retrieval strategy over the alternative, what breaks first at ten times the traffic, or how they would know the ranking model had quietly degraded. ML system design interviews are decided in that second layer of questioning, which is exactly the layer that reading design write-ups does not rehearse.
Fifteen minutes later, you have three things.
- A map of what was actually checked
- A gap traced back to what you said
- One thing to study tonight
Design decisions get probed, not accepted
When you say you would use a vector store, the follow-up asks what you would measure to know retrieval was the problem. When you propose a cache, it asks what invalidates it. The session treats every choice you name as a claim that has to hold up, which is how a real design interview behaves.
- Retrieval, ranking, and re-ranking trade-offs
- Latency budgets and where they are actually spent
- Evaluation design — offline metrics versus online behaviour
- Failure modes, degradation, and what you would monitor
Built for AI/ML system design, not generic distributed systems
Generic system design preparation covers load balancers and sharding. ML system design turns on questions those materials never reach: how you evaluate a retrieval component separately from generation, what a training-serving skew looks like in practice, and how you tell model degradation apart from a data pipeline break.
You leave with one concept, not a critique
The result is a coverage map of the design areas the conversation reached, one possible gap with the reasoning that produced it, and a single next action. Not a graded design review — one thing worth studying, and the evidence for why it is that one.
Nothing to install. Talk it through, keep the map.
Before you start
- Does this cover general system design interviews?
- No. It covers ML and LLM system design specifically — retrieval, ranking, evaluation, serving, and monitoring. General distributed systems interviews are outside what the diagnostic tests.
- Do I need to draw a diagram?
- No. The session is voice-first and evaluates how you reason about the design out loud, which is where design interviews are actually decided.
- What roles is this scoped to?
- Design work inside ML, LLM, Applied AI, AI infrastructure, and MLOps. Rounds belonging to other disciplines fall outside what it can probe, and rather than improvising a result for them it returns nothing at all.
- Do I need to pay to try it?
- No. Accounts carry 15 free minutes that renew each week, which is room to walk one design end to end without entering a card. Running more designs than a week allows costs $29 a month.
Find the concept your prep has not tested.
15 free minutes every week. No card. One gap map, one next action.