Reddit Machine Learning Engineer Interview Questions

Feature Store ## Problem Statement Design a feature store for Reddit's ML platform. Explain how offline and online feature storage stay consistent, how features are computed and materialized, how tr

Video Recommendation ## Problem Statement Design a video recommendation system for Reddit. Focus on how candidate generation, ranking, serving, and feedback loops work end to end, and how user inter

ML Fundamentals ## Problem Overview This round is a fundamentals-heavy Machine Learning Engineer discussion. The interviewer typically starts with a simple supervised learning setup, then uses a plo

Post Click Prediction ## Problem Overview This Reddit Machine Learning Engineer interview is a practical tabular modeling exercise done in a Jupyter notebook. You are given a clean JSON dataset wher

The 75-minute interview was structured as follows: * **Problem Definition (5 min):** Introduction to the task and problem scope. * **ML Implementation (50 min):** A timed coding session requiring scre

## Problem You are asked to build a CTR (click-through rate) prediction model for a content recommendation system. Walk through the full ML modeling process: **1. Problem framing** - Binary classification: will user click on item? (positive = click) - Training signal: implicit feedback (clicks), with heavy class imbalance (~1% CTR) **2. Feature engineering** - User features: historical CTR, session recency, demographics - Item features: category, age, historical CTR - Context features: device, time-of-day, position bias **3. Model options** - Logistic Regression (baseline, interpretable) - Gradient Boosted Trees (GBDT) for tabular features - Deep factorization machines or two-tower neural model for large sparse IDs **4. Evaluation** - Metrics: AUC-ROC, log-loss, calibration - Why accuracy is a poor metric at 1% CTR ## Follow-ups 1. How do you handle position bias in training data (items shown higher get more clicks)? 2. How do you evaluate the model offline before an A/B test? 3. The model's calibration drifts after 2 weeks — what causes this and how do you fix it?