Reddit Machine Learning Engineer interview questions

23 Machine Learning Engineers have shared notes from their Reddit interviews, with the questions they were asked in each round. That is 44% of the 52 Reddit interview notes on Screna AI.

23 Machine Learning Engineer interview notes

Reddit·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Prepping for a Reddit ML Engineer interview and trying to figure out how to answer the AI-in-daily-work behavioral question without sounding like a press release.
  • How do you incorporate AI tools into your day-to-day work?

“Every time I try to answer this I end up listing tools like I'm reading off a spec sheet.”

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Reddit·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for an ML Engineer role at Reddit. Two questions, both the classic failure/success combo. Nothing surprising about the format but the bar for specificity felt higher than I expected.
  • Describe a time something didn't go as planned in your work. What happened, what did you take away from it, and what would you change if you could do it over?
  • Walk me through a time you succeeded at something. What specifically did you contribute and what was the measurable impact?

“I went with an incident from a model rollout that caused some downstream noise in a recommendation pipeline.”

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

Jun 2026
Reddit ML Engineer interview with a product-sense style question that felt a bit out of left field for the role. One question, pretty open-ended, and I left unsure whether I'd framed it the right way for an MLE position versus a PM one.
  • How would you redesign the onboarding experience for new users on a community platform like Reddit, covering friction identification, success metrics, prioritization, and experiment design?

“Spent the first few minutes trying to figure out whether they wanted an MLE answer or a PM answer.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Reddit ML Engineer interview with a linked list problem that looks straightforward but has a few gotchas if you're not careful about pointer manipulation.
  • Given the head of a singly linked list, reorder it so all odd-indexed nodes come first followed by all even-indexed nodes, preserving relative order within each group. Must run in O(n) time and O(1) space.

“I knew the general idea pretty fast: keep two pointers, one crawling odd nodes and one crawling even, then stitch the even chain onto the end of the odd chain.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Reddit ML Engineer interview, at least one round involved a coding problem around designing a tennis scoring system from scratch. More OOP-heavy than I expected for an ML role, felt like a general SWE screen dressed up as something else.
  • Design and implement a tennis scoring system as a class. It should track points for two players, return scores in both raw and human-readable formats (Love, 15, 30, 40, Deuce, Advantage, Game), handle the win condition (4+ points with a 2-point lead), prevent scoring after the game ends, and reset internal state during deuce cycles to keep things bounded.

“This took longer than I thought it would.”

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

Jun 2026
Reddit ML Engineer interview focused entirely on a system design problem for their home feed ranking pipeline. Pretty technical and open-ended, the kind of question where you can go in a hundred directions and have to pick your battles.
  • Design a complete end-to-end ML system that exposes a method taking a user ID and a list of post IDs, and returns a probability score per post representing how likely that user is to comment on each post. The system needs to cover both model training and real-time serving at scale.

“This one is deceptively wide.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Reddit ML Engineer interview, part of a multi-part coding exercise where they had you keep extending a tennis scoring system. The side-switching follow-up in part 3 was the most interesting design piece and probably where most people trip up.
  • You have a working tennis game implementation. Now modify it so players switch sides between games, following the standard rule where sides swap after odd-numbered games. Side is presentational only and doesn't affect scoring. Where do you track this state and why?

“The 'where do you track it' part is what they actually care about.”

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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·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·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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