ml-system-design-interview

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.

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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

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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.

flowchart TD
    R[1. Requirements\n- Business goal\n- Users and scale\n- Latency/throughput SLA\n- Constraints] --> M[2. Metrics\n- Offline: precision, recall, NDCG\n- Online: CTR, conversion, revenue\n- Guardrails: latency p99, fairness]
    M --> D[3. Data\n- Sources and collection\n- Labeling strategy\n- Pipeline: ETL, validation\n- Freshness and staleness]
    D --> F[4. Features\n- Engineering and transforms\n- Feature store architecture\n- Online vs offline features\n- Freshness requirements]
    F --> Mo[5. Model\n- Architecture selection\n- Training pipeline\n- Iteration strategy\n- Baseline and ablation]
    Mo --> S[6. Serving\n- Batch vs online vs streaming\n- Caching and precomputation\n- Scaling and cost\n- Canary and shadow mode]
    S --> Mon[7. Monitoring\n- Data drift detection\n- Model degradation alerts\n- A/B testing framework\n- Rollback strategy\n- Feedback loops]
    Mon -.->|Feedback loop| D
    Mon -.->|Retrain trigger| Mo