Reddit Machine Learning Engineer Interview Questions
6+ questions from real Reddit Machine Learning Engineer interviews, reported by candidates.
Round Types
Top Topics
Questions
Feature Store
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
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
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?
What Reddit Looks for in Machine Learning Engineer Interviews
Reddit Machine Learning Engineer interviews are calibrated against the level and scope expected of the role. Across 6+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Reddit Machine Learning Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Reddit's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Reddit Machine Learning Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Reddit Machine Learning Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Reddit Machine Learning Engineer interview retrospectives on LeakCode.
See All 6 Reddit Machine Learning Engineer Questions
Full question text, answer context, and frequency data for subscribers.
Get Access