Machine Learning Engineer Interview Questions & Answers (2026)

Problem framing → data collection and cleaning → feature engineering → model selection and training → evaluation → deployment → monitoring and retraining. Each stage has feedback loops; the work is mostly data and iteration, not just modelling.

Creating informative inputs from raw data: scaling/normalisation, encoding categoricals, handling missing values, creating interactions and aggregations. Good features often improve performance more than a fancier model.

Use appropriate metrics (precision/recall, F1, PR-AUC), resampling (oversample minority/SMOTE, undersample majority), class weights, or threshold tuning. Choose based on the business cost of false positives vs false negatives.

Split data into k folds, train on k-1 and validate on the remaining fold, rotating k times and averaging the score. It gives a more reliable performance estimate than a single split and helps detect overfitting.

A penalty on model complexity to reduce overfitting. L1 (Lasso) adds the absolute value of weights and can zero them out (feature selection). L2 (Ridge) adds squared weights and shrinks them smoothly. ElasticNet combines both.

Random forests train many independent trees on bootstrapped data and average them (bagging, reduces variance). Gradient boosting builds trees sequentially, each correcting the previous one’s errors (reduces bias). Boosting is often more accurate but needs careful tuning.

Batch learning trains on the full dataset periodically; online (incremental) learning updates the model as data arrives, suiting streaming data and concept drift. Online learning needs care to avoid instability.

Track operational metrics (latency, errors), data quality and drift, prediction distribution, and business/accuracy metrics where labels arrive. Set alerts and a retraining trigger; log inputs/outputs for debugging.

A central system to define, compute, store and serve features consistently for training and inference, avoiding training/serving skew and enabling feature reuse across teams.

Precision = TP/(TP+FP): of predicted positives, how many were right. Recall = TP/(TP+FN): of actual positives, how many were caught. F1 is their harmonic mean, useful when you need a balance and classes are imbalanced.