Agent skill · research science · erichowens
ml-system-design-interview
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding,
Why this skill is useful
Provides structured frameworks and specific tradeoff analysis techniques for ML system design that the AI wouldn't generate reliably on its own.
What it needs
About 8k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
ML System Design Interview End-to-end ML pipeline design coaching for staff+ engineers. Covers the full arc from problem definition through production monitoring -- the scope expected at L6+ interviews at top-tier ML organizations. This skill assumes 15+ years of ML/CV/AI/NLP experience. It does not teach fundamentals. It structures the knowledge you already have into the format interviewers reward. --- When to Use Use for: Practicing 45-minute ML system design rounds Structuring whiteboard presentations for recommendation, ranking, RAG, fraud, perception systems Analyzing serving architecture tradeoffs (batch vs online vs streaming) Identifying L6+ differentiation signals (problem ownership, org constraints, data flywheels) Reviewing and critiquing ML system design answers NOT for: Coding interviews (use senior-coding-interview) Behavioral / leadership questions (use interview-loop-strategist) ML theory or math derivations Implementing models or writing training code Paper reading or research review --- The 7-Stage Design Framework Every ML system design answer follows this arc. The stages are sequential but you will loop back as constraints emerge. The Mermaid diagram below is your whiteboard skeleton. Stage Details Stage 1 -- Requirements (5 minutes) Ask clarifying questions before designing anything. Establish: Who is the user? What is the business metric? What is the latency SLA? What scale (QPS, data volume)? What are hard constraints (cost, privacy, regulation)? An L6+ candidate owns the problem definition -- do not wait for the interviewer to hand you requirements. Stage 2 -- Metrics (3 minutes) Define offline metrics that you can measure before deployment AND online metrics that matter to the business. Explain the gap: "NDCG improvement offline does not always translate to CTR lift online because of position bias and novelty effects." Define guardrail metrics: latency p99, fairness across user segments, cost per prediction. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/ml-system-design-interview