Machine Learning Interview Prep Roadmap

Focus on bias-variance trade-off, regularization, evaluation metrics, and error analysis. Interviewers often probe why a model failed, not just what model you chose. Being able to diagnose a model from learning curves, confusion matrices, and residual plots is as important as knowing which algorithm to pick.

Use small case studies to explain your reasoning from data cleaning to model selection. 'Given a churn prediction problem with 95% non-churn vs 5% churn — how would you approach the class imbalance and which metric would you optimize?' is a more realistic interview question than 'explain gradient descent'.

Expect questions on feature pipelines, data drift monitoring, retraining triggers, and serving latency. For senior ML roles, system design rounds often focus on designing components like a recommendation system, a fraud detection pipeline, or a content moderation system at scale.

Be ready to discuss offline versus online metrics and how to detect silent model degradation — when a model's accuracy on the validation set stays high but its real-world performance degrades because the distribution has shifted. This is a common production failure mode that distinguishes applied ML practitioners from researchers.

Most production ML work involves running experiments. Interviewers at product companies (Meta, Google, Flipkart, Swiggy) frequently test your ability to design a statistically sound A/B test, detect novelty effects, handle heterogeneous treatment effects, and report results to non-technical stakeholders.

Know when not to use A/B testing: when sample sizes are too small, when the effect size is too small to detect in a reasonable time window, or when ethical constraints require a rollout rather than a split test.