If you're interviewing for ML roles this year, the LLM loop has quietly changed. Here are the three question types I see most often in 2026 interview reports — and how I'd practice each.

1. Coding with an LLM twist

LeetCode-style rounds are still there, but interviewers now add LLM-flavored constraints: "implement a tokenizer," "write a top-k sampler with temperature," or "cache-friendly attention." The coding part is easy; the hard part is narrating your choices under time pressure.

Practice tip: solve the problem first, then re-solve it while explaining every decision out loud. Interviewers score communication as much as correctness.

2. LLM system design

"Design a RAG pipeline," "design an eval harness," "design a serving system for 10k QPS." These questions test whether you can decompose a fuzzy problem into components: data ingestion, retrieval, ranking, generation, evaluation, monitoring.

Practice tip: always start by scoping — ask about QPS, latency budget, data freshness.…