Interview guide
ML interviews layer theory (bias-variance, evaluation metrics), applied case design ('build a recommender'), and increasingly ML-systems questions — with LLM-era roles adding RAG and evaluation design prompts.
Fundamentals: bias-variance, overfitting, regularization, cross-validation
Metrics: precision/recall trade-offs, ROC-AUC, when accuracy misleads
Case design: fraud detection, recommendations, churn — end to end
Feature engineering and data leakage traps
ML systems: serving, monitoring, retraining, drift
LLM topics: RAG design, fine-tuning vs prompting, eval construction
For every case question, start with the label definition and evaluation metric before touching models — interviewers screen for that ordering because it's what separates production ML thinking from coursework.
ML interviews layer theory (bias-variance, evaluation metrics), applied case design ('build a recommender'), and increasingly ML-systems questions — with LLM-era roles adding RAG and evaluation design prompts.
For every case question, start with the label definition and evaluation metric before touching models — interviewers screen for that ordering because it's what separates production ML thinking from coursework.
Directly — interviewers build questions from your resume's claims, so every skill and achievement listed becomes fair game. A tailored, honest resume steers the interview toward your strongest material; an inflated one hands the interviewer traps you set for yourself.
Interviewers build their questions from what your resume claims. Tailor it to the role honestly first — free ATS score, missing keywords, and a recruiter-ready rewrite.