Code Quality

In this section, we focus on code quality, a crucial yet often underestimated element of ML interviews. Even if our model works well, poorly written code can seriously damage our impression. Clean, clear code reflects professional maturity and real-world readiness.

• Mediocre code may be functional but is hard to read—characterized by cryptic variable names, long repetitive functions, unclear logic, and little to no documentation.
• Stellar candidates write clean, organized code, breaking it into logical chunks with reusable functions and clearly labeled sections.
• We use meaningful variable names, write comments that explain the "why" behind decisions, and maintain a logical flow from start to finish.
• Our code is efficient in design, avoiding unnecessary complexity and maintaining smooth execution without over-optimizing.
• Ultimately, we recognize that code is communication—and polished, maintainable code signals we’re ready to contribute to real engineering teams.

If you want to learn even more from Yayun:

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