I bombed my first ML interview. Completely.

They asked me to design a recommendation system at scale and I rambled about model architectures for 20 minutes without once mentioning data pipelines, serving infrastructure, or monitoring.

The interviewer politely said, "We need engineers, not researchers."

That hurt. But it taught me what ML interviews actually test in 2026:

The coding round is still LeetCode Medium — no surprises there. ML theory is standard (bias-variance, regularization). But then comes LLM system design, which is now mandatory. "Design a RAG system for internal documents." "How would you evaluate an LLM-powered customer service agent?"

The biggest change from a few years ago: they want to hear about production failures. "Tell me about a time your model broke in production" is basically guaranteed. And they want real answers, not polished stories.

After 1900+ LeetCode problems I can tell you — preparation compounds. But don't just grind problems. Practice explaining your design decisions out loud. That's what actually gets you the offer.