Why Coding Matters In ML

In this new section on coding fundamentals, we explore why strong programming skills are essential for machine learning engineers. While ML involves data and models, it’s also deeply rooted in real-world software engineering.

• We clarify a common myth: ML engineers are still engineers, and are expected to meet the same coding standards as traditional software engineers.
• Our software engineering background is an asset—if applied correctly, it can set us apart from more research-focused ML candidates.
• Coding matters in ML because models must be integrated into production systems, maintained, and optimized for performance and scalability.
• We’re reminded to consider algorithmic complexity, especially when building pipelines or serving models at scale.
• The key takeaway: don’t underestimate LeetCode-style coding prep. Many ML interviews, especially at top tech companies, still include traditional algorithm and data structure questions. Strong coding skills are non-negotiable.

If you want to learn even more from Yayun:

• Book 1 on 1 career guidance with her here: https://calendly.com/your-friend-yayun/1on1
• Follow her on LinkedIn: https://www.linkedin.com/in/yayunjin/