ml-system-design-interviewer

What problem does it solve?

This skill provides a realistic, structured ML system design interview experience modeled after FAANG-level interviews, helping candidates practice thinking beyond model accuracy to data, pipelines, and production concerns.

Core Features & Use Cases

• Persona-driven interview sessions that emphasize end-to-end systems design, including data pipelines, feature stores, model serving, monitoring, and retrieval.
• Phase-based interview structure (Requirements & Scope, Data & Features, Training & Serving, Monitoring & Drift, with adaptive difficulty and hints) to build comprehensive problem-solving skills.
• Scorecard generation and feedback, plus references and resources to guide improvement; supports remote practice with consistent prompts.
• Visual and interactive prompts that guide candidates through architecture diagrams, trade-offs, risk assessment, and guardrails.

Quick Start

Initiate a mock interview by invoking the ML System Design Interviewer and follow the Phase 1 prompts to kick off the session.