Role-Specific System Design Focus

In this part of the system design section, we explore how ML system design interviews vary depending on the role and level we’re applying for. Knowing what’s emphasized helps us tailor our preparation strategically.

• For product-focused ML roles, we focus on modeling choices, feature engineering, and connecting ML decisions to tangible business impact.
• ML infrastructure roles emphasize architecture, scalability, reliability, and serving systems—including distributed training and feature stores.
• Research-oriented roles dive into experimentation frameworks, novel modeling techniques, and how to transition prototypes into production.
• In ML generalist roles (common at startups or smaller teams), we’re expected to demonstrate a balanced understanding across the full ML lifecycle.
• Importantly, interview expectations are tied more to seniority level than company size—a senior role at a startup may involve deeper system questions than a junior role at a large tech company.

Next, we’ll learn a system design framework that can flex across these scenarios and help us organize our thinking effectively.

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