Getting an ML engineer role at a top tech company requires preparation across four distinct areas. This guide breaks down exactly what to study, how to prepare, and what interviewers are actually looking for.
Phase 1
ML Theory & Algorithms
- ✓Know the maths behind Linear and Logistic Regression — gradient descent, loss functions, regularisation
- ✓Be able to explain any algorithm in 60 seconds using plain English, then go deeper if asked
- ✓Bias-variance tradeoff — be able to diagnose overfitting and underfitting from learning curves
- ✓Evaluation metrics — know when to use accuracy vs F1 vs AUC, and why
- ✓Ensemble methods — Random Forest, XGBoost, AdaBoost — explain the key differences
Phase 2
Coding & Python
- ✓Implement K-Means, KNN, linear regression from scratch using only NumPy
- ✓Be fluent with Pandas — groupby, merge, pivot, handling missing data
- ✓sklearn Pipeline — understand why it is essential and how to use it correctly
- ✓Feature engineering — log transforms, one-hot encoding, target encoding
- ✓SQL — GROUP BY, window functions, subqueries are commonly tested
Phase 3
ML System Design
- ✓Use the SCALE framework: Scope, Components, Algorithms, Learning, Evaluation
- ✓Classic problems: recommendation system, fraud detection, content moderation, ETA prediction
- ✓Always discuss: data collection, feature engineering, model choice WITH justification, deployment, monitoring
- ✓Know how to handle class imbalance, cold start problems, concept drift
- ✓Mention A/B testing and how you would measure success in production
Phase 4
Behavioural & Communication
- ✓Use STAR method: Situation, Task, Action, Result for all experience questions
- ✓Have 3 strong ML project stories ready — one success, one failure with learnings, one collaborative
- ✓Practise explaining complex concepts to a non-technical audience
- ✓Show intellectual curiosity — ask thoughtful questions at the end
- ✓Google specifically asks about data ethics and fairness — be prepared
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