I went through the Google MLE interview for an L5 role, Mountain View, in Q1 2026. The team was in the Ads space. Sharing this because I had to piece together a picture from 4-year-old posts and the process has shifted.
The loop was 5 rounds:
Coding (x2): Standard SWE algo. They don't give you ML-specific problems in the coding rounds. One was a classic heap problem, one was a DP problem that I thought I was going to botch but recovered. Google MLE coding difficulty is the same as SWE. If you're rusty because you've been heads-down in PyTorch for two years, get on Neetcode immediately. This is not optional.
ML Design: Designing a click-through rate prediction system from scratch. Covered: problem framing (binary classification), feature engineering for sparse categorical features (user/item IDs), model choice (logistic regression vs. GBDT vs. neural), training pipeline, offline vs. online evaluation, and deployment. They went deep on calibration of probability estimates, which was a good sign the interviewer knew their stuff. For Ads-focused roles, know the basics of how ad auction mechanics interact with CTR models.
ML Theory: Mix of ML fundamentals. Bias-variance tradeoff, gradient descent variants, what happens when your training set has class imbalance, precision vs. recall tradeoffs. Then a question about how you'd debug a model that performs well offline but degrades fast in production (concept drift). That last question is where I've seen people stall. Have a mental framework for diagnosing model degradation.
Behavioral: Googleyness. Conflict, leadership, ambiguity. Nothing MLE-specific. Bring strong STAR stories.
The interview took about 6 weeks from recruiter screen to offer. The ML design round was definitely where I felt most evaluated. TC came in around $380k at L5 for Mountain View. I expected more given market comps I'd seen online but the team and scope felt right.
Feel free to ask about specific rounds. The ML design one had a lot of ground to cover.