Reddit Interview Questions — Page 4

Reddit·Machine Learning Engineer·Technical Phone Screen

May 2026

This was a coding round for a Machine Learning Engineer role at Reddit, focused on object-oriented design for a tennis scoring system. The problem was split into progressive parts and required clean composition patterns and some careful thinking about state management across games.

Design a set-level scoring system for tennis that tracks multiple games, exposes methods to get the current set score and determine the set winner, and supports both simplified and standard scoring formats.
How do you ensure the game score resets correctly after each completed game, and that cross-game win counts accumulate properly across the set?

“The composition angle was the key thing here.”

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Reddit·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Reddit MLE system design round focused almost entirely on building a video recommendation system end to end. Way more infrastructure and logging depth than I expected, less ML theory than I prepped for.

Design a video recommendation system from scratch, covering candidate generation, ranking, and online serving with low latency requirements.
How would you design the feature store for this recommendation system, and how do you handle online versus offline feature consistency?
Walk me through how you'd design the data and event logging pipeline to capture user interactions like impressions, clicks, and dwell time.
How do you join impression, click, and dwell time signals to construct training data, and what pitfalls do you watch out for?
How would you monitor a live recommendation system and detect when model quality is degrading in production?
How would you structure an A/B testing and experimentation framework for iterating on recommendation models safely?

“I started with a two-tower retrieval setup for candidate generation and moved into a ranking layer with a lightweight gradient boosted model for latency reasons.”

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Reddit·Data Scientist·Technical Phone Screen

Jun 2026

Reddit data scientist interview that went deep on causal inference, specifically synthetic control methods. One long technical question that covered a lot of ground, felt more like a dissertation defense than a typical DS screen.

Walk through the core identification assumptions behind synthetic control, and explain how each one, if violated, would distort your treatment effect estimates.
How do you select predictor variables and lag periods for synthetic control without accidentally leaking post-treatment information into your model?
Describe your full inference strategy for synthetic control: how do you construct treatment effects, generate p-values, and build uncertainty bands?
What diagnostics do you run when pre-period fit is poor, and what are your options for fixing it or switching to a different method entirely?

“I knew convex hull and no interference but fumbled explaining why pre-period fit matters as a proxy for unobserved confounders.”

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Reddit·Data Scientist·Hiring Manager Screen

Jun 2026

Reddit DS interview focused almost entirely on cross-functional dynamics, specifically how you work with PMs and engineers on an ads team. No coding, no SQL, just behavioral and product judgment. Felt more like a hiring manager conversation than a technical screen.

How do you structure collaboration with Product Managers and Engineers, covering things like requirements, timelines, and who owns what?
How do you decide when to push back on a request versus proactively driving something yourself?
Walk me through a specific example where you shaped product direction or stopped a bad decision from moving forward.

“I rambled a bit here.”

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Reddit·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Reddit ML Engineer system design round, one big open-ended question about building a comment-prediction ranking signal end-to-end. The scope was massive and the follow-ups kept coming once I thought I was done.

Design a full ML system for an API that takes a user ID and up to 1,000 candidate post IDs and returns a score per post representing the probability that the user would comment on each post if shown it.
Your training data comes almost entirely from what the production ranker already chose to show. How much exploration traffic do you actually need, and how do you limit the cost to user experience?
The comment score gets blended daily with other objectives like upvotes and dwell time. How do you keep it calibrated across model retrains so the blend weights don't drift?
Optimizing for comments can reward outrage-bait and controversial content. How would you detect that the model is learning that shortcut, and what would you do about it?
Comment velocity on a popular post changes minute to minute. How do you keep those features fresh in online serving without creating skew against the batch features used during training?

“This is the main question and it ate the whole session.”

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Reddit·Software Engineer·Technical Phone Screen

Jun 2026

Reddit SWE interview with a billing domain coding problem. The question was more involved than I expected, lots of moving parts with event ordering and status transitions.

Given a per-account history of billing events (charges, payments, refunds, adjustments, due dates), implement a system that computes the current billing status for each account. Statuses include things like CURRENT, PAST_DUE_30, PAST_DUE_60, DELINQUENT, and IN_COLLECTIONS, based on rules around time windows, grace periods, and payment ordering. Events must be processed in chronological order, and you should output the final status per account, plus any status transitions.

“I underestimated this at first.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Reddit ML engineer interview, live coding round in a Jupyter notebook on a click-through rate prediction problem. The dataset had pet category time-spent features plus a tag column, and you had to go from raw EDA all the way to model comparison and a discussion about what you'd actually do in production. Pretty thorough for a single session.

Given a dataset with per-post features for time spent on different pet categories, a current tag, and a click outcome, walk through an exploratory data analysis using pandas and seaborn.
Train a model to predict click-through rate and compare at least two different modeling approaches.
How would you handle the categorical 'current_tag' feature, and what feature engineering options would you consider?
How would you validate your model, and what evaluation metric would you choose between log-loss and AUC?
How would you handle class imbalance in the click-through dataset?
Given more time, what would you do next to improve or productionize this model?

“sns.pairplot is basically expected here so I led with that, then did class balance check since is_click is almost always imbalanced in practice.”

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Reddit·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Reddit for a software engineering role. The whole session was basically one big deep dive into leaderboard architecture, which sounds contained but spiraled into a lot of territory fast.

Design a real-time game leaderboard that supports score updates, top-K queries, rank lookups, range queries (e.g. players ranked around a given position), and multiple leaderboard scopes like per-region or per-time-window.

“I started with Redis sorted sets because that felt obvious, but then the follow-ups kept coming and I realized I hadn't thought hard enough about the sharding story.”

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Reddit·Machine Learning Engineer·Onsite - Product Sense / Strategy

Jun 2026

Reddit MLE interview that threw a product sense curveball. The main question was a full onboarding teardown for new Reddit users, which felt odd for an ML role but apparently they do this.

How would you improve Reddit's onboarding experience for new users? Cover who these users are and what they want, where the current flow breaks down, two or three concrete improvements you'd prioritize, and how you'd measure success and run experiments.

“Not what I expected from an ML round.”

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Reddit·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Reddit PM interview, single cross-functional strategy question about working across teams to influence video ranking. Pretty lean on details but the question itself has some real depth to it.

You're a PM on Reddit's video team. How would you partner with the Feeds team to get videos ranked higher in the feed?

“This one tripped me up a bit because it sounds like a product strategy question but it's really a cross-functional alignment question in disguise.”

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