How I prepare for ML interviews

The four buckets

Most ML interview loops test some mix of four things. I prepare each one deliberately rather than grinding randomly:

1. ML breadth — bias/variance, regularization, common algorithms, when to use what, and why.
2. ML depth — one or two areas you can go deep on (e.g. NLP, recsys, computer vision, LLMs).
3. Coding — standard DS&A, plus the occasional "implement this from scratch" (k-means, a small neural net, attention).
4. ML system design — designing an end-to-end ML system: data, features, model, serving, monitoring, feedback loops.

My weekly loop

Consistency beats intensity. A steady loop for 4–6 weeks beats a frantic weekend.

• Mon/Wed/Fri — coding practice (45–60 min).
• Tue/Thu — read + take notes on one ML topic, then explain it out loud.
• Weekend — one full ML system design mock, written up end-to-end.

A note on "explaining out loud"

The single highest-leverage habit: after reading something, close the tab and explain it as if to a colleague. If you stumble, you don't actually know it yet. This surfaces gaps faster than re-reading ever will.

Check out the ML interview prep page for the resources I use in each bucket.