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Reddit interview notes, page 1 of 6

Large Enterprises

Most of these Reddit write-ups come from Machine Learning Engineers (23), then Software Engineers (21) and Data Scientists (5). The most reported rounds are the technical phone screen (29), the system design round (14), the product sense round (5) and the behavioral / leadership round (3).

52 interview notes · updated Jul 2026

Reddit·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round for an ML Engineer role at Reddit. Just one question but it was basically two questions stitched together, and I felt like I was rambling by the end.
  • Walk me through your biggest failure, including what caused it and what you took away from it, and then your biggest professional success, including your specific role in it and why it worked.

“Two-part question and I did not pace myself well at all.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Reddit ML engineer interview with a systems coding problem that felt more backend than ML. The task was to implement a simplified memcached protocol from scratch, which wasn't what I expected going in.
  • Implement a simplified memcached-style key-value server over TCP that supports get and set commands using a line-based ASCII protocol.

“Spent the first few minutes just parsing the protocol spec in my head, which was probably visible.”

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Reddit·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Reddit system design round, one big question about building a distributed job scheduler from scratch. The scope was wide enough that I kept second-guessing whether to go deeper on one area or keep moving. Came away feeling like I covered maybe 70% of what they wanted.
  • Design a distributed job scheduler that supports user-submitted jobs with schedules, priorities, and dependencies, executing them across a pool of workers. Your design should cover cron-style scheduling, DAG-based dependencies, retries, idempotency, worker assignment, fault tolerance, scaling, and monitoring.

“This is one of those questions where the surface area is so big you have to make a call early: go broad and skim, or go deep and risk missing chunks entirely.”

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Reddit·Software Engineer·Technical Phone Screen

Apr 2026
Got a coding question from Reddit that seemed straightforward until I actually had to think through all the edge cases. Tennis scoring is deceptively annoying to implement cleanly.
  • Implement a tennis match scoring system. Given a sequence of points won by each player, output the human-readable score at each point (Love, 15, 30, 40, Deuce, Advantage) and track the game, set, and match state correctly, including deuce and advantage logic.

“I started with the basic point progression and felt fine until deuce came up.”

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Reddit·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Reddit ML infrastructure design round, basically a full system design for a comment-likelihood prediction platform. Pretty open-ended but they were clearly looking for depth on feature stores and serving architecture, not just hand-waving at 'use Kafka'.
  • Design an end-to-end ML platform that predicts the likelihood a user will comment on a post, covering feature pipelines, a feature store, training, online inference, and production monitoring.
  • How would you handle training/serving feature skew in this system?
  • How would you approach cold start for new users or newly created posts?
  • Walk through how you'd support A/B testing and safe rollout for a new model version.
  • What monitoring would you put in place, and what would trigger a model retraining?

“This was the whole interview basically.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Apr 2026
ML theory round at Reddit for an MLE role. Three questions, all conceptual, no coding. The distribution plot one was the kind of thing that sounds easy until you're actually staring at it and second-guessing yourself.
  • Given two variables x and y, how would you model their relationship and why?
  • You're shown a plot with two overlapping distributions. What do you think is going on, and what would you do about it?
  • How would you handle cold-start problems for both new users and new items in a recommendation system?

“Open-ended enough that I rambled a bit before landing anywhere useful.”

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Reddit·Data Scientist·Technical Phone Screen

Apr 2026
Reddit data scientist interview with a probability question that sounds like a stats class warmup but has enough moving parts to trip you up if you're not careful. One question, math-heavy, with an optional coding component.
  • You roll two fair dice independently: die A has m faces and die B has n faces, each labeled starting from 1. Derive a closed-form expression for the probability that A lands higher than B, as a function of m and n. Bonus: write a Python function to compute it.

“I knew the setup immediately but fumbled the algebra for longer than I'd like to admit.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Reddit MLE interview that covered the full spectrum from basic modeling decisions to distribution analysis to cold start problems. Nothing too surprising but the distribution plot question had more depth to it than I expected.
  • You have a dataset with one input feature and a target variable. How do you decide how to model it, what baselines do you try first, and what metrics do you use?
  • You're shown a plot with two distributions, maybe two classes or train vs production. Walk through how you'd interpret it and what you'd do next.
  • What are your practical strategies for handling cold start for new users, new content items, and new regions or languages?

“Felt like a warmup but it's actually easy to overthink.”

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Reddit·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Reddit system design round for a software engineer role, focused entirely on building a leaderboard service from scratch and scaling it up. The progression from a toy single-server version to a distributed, eventually-consistent system with a friends-filter variant made this one of the more thorough design interviews I've sat through.
  • Design a leaderboard service that tracks the top-K scores across all users, starting from a minimal single-server setup with low traffic and no social filtering.
  • How would you scale this leaderboard to handle much higher read and write throughput? Walk through your partitioning and replication strategy.
  • Now extend the design to support a 'friends leaderboard' where a user can see the top-K scores only among their friends. What changes?

“I started with a sorted set in Redis and explained why a skiplist makes inserts and rank queries both fast.”

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Reddit·Machine Learning Engineer·Technical Phone Screen

Apr 2026
First round at Reddit for an MLE role, focused entirely on ML fundamentals with a CVR modeling problem. Pretty open-ended, which I wasn't expecting for a round labeled 'fundamentals'.
  • How would you design a conversion rate prediction model from scratch?

“Spent the first few minutes just trying to scope the problem, which features matter, what counts as a conversion, what the label distribution looks like.”

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