AI/ML System Design
Design a production AI system from a fuzzy customer brief. Architecture, data, retrieval, evaluation. The interviewer pushes on every decision and asks what breaks at 100x.
- Scale
- Robustness
- Trade-offs
Drop in any FDE job description. Laoh generates the full three-round interview tailored to that company, then grades you the way real FDE recruiters do.
Two operators who've built training and assessment infra at scale. Twice before. Different workforce categories. Now solving it for FDEs, the defining role of the AI era.
Three rounds, three rubrics, all shaped by the company you're walking into, not a generic template.
Design a production AI system from a fuzzy customer brief. Architecture, data, retrieval, evaluation. The interviewer pushes on every decision and asks what breaks at 100x.
Build inside a real codebase, paired with AI agents. Ship something the customer could actually deploy. Laoh flags every place you could have leaned on agents, skills, or MCP and didn't.
Live video roleplay with an AI client. Defend your decisions. Handle pushback. Tell the story. Laoh corrects you in real time when you bury the answer.
Every round produces a numbered scorecard against the actual evaluation dimensions FDE recruiters use. No vibes. No "you did great!". Just the gaps.
Add file context up-front and specify acceptance criteria. The model is filling in defaults that may not match your intent. You skipped MCP integration on the database step where it would have saved 4 calls.
Not generic LeetCode. Not generic system design. The interview Laoh generates matches the actual loop that company runs.
We work with the people who design these interviews at frontier AI labs. The rubric Laoh uses is theirs, not ours.
Every round produces a numbered scorecard against the actual evaluation dimensions. You'll know exactly where you'd fail before they tell you.
Beta is opening in batches. Get on the list.