Three candidates describe Reddit ML engineer interviews built around a hands-on modeling task on a dataset. Reports also mention ML concepts and behavioral rounds, and one candidate describes a four-round onsite with two DSA rounds.
$245K
Avg. Base Comp
$420K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Across three candidate reports, the common thread in Reddit's ML engineer interviews is a practical modeling task on a dataset. One candidate was asked to write a binary classification model and answer follow-up questions about the approach. Another had 45 minutes to build a model from a dataset. A third finished the coding round so quickly that the interviewer moved on to a full systems design question.
Speed and fluency seem to matter. The candidate with the 45-minute limit said the task was not especially hard but the clock was tight. Because they had been told searching was allowed, that candidate leaned on web searches for model setup, and in hindsight felt the interviewers expected them to choose and tune an approach without searching. Practice building a model end to end with scikit-learn from memory, including choosing and tuning an approach.
The other rounds vary between reports. One candidate described a four-round onsite: two DSA rounds, an ML fundamentals round, and a hiring manager behavioral round. They called the DSA problems harder to parse than typical LeetCode, so practice restating a problem and asking clarifying questions before coding. The ML fundamentals questions included tradeoffs such as logistic regression versus random forests. Another candidate reported an ML concepts round and a behavioral interview but gave few details.
All three reports ended without an offer. One candidate was told the original role had been filled and then heard nothing after repeated follow-ups. Keep a record of recruiter communication and ask about next steps directly.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Reddit, Inc. process.
I'll admit the Googling rule caught me off guard. The recruiter said searching was allowed during the coding portion, so I took that at face value and leaned on the web for model setup I could have written from memory. Looking back, the expectation was that I'd pick the right approach and tune it myself, and I didn't read that signal. If you go in with the same assumption, know that the interviewers may not want you to search at all, even when you've been told you can.
The process had four stages. The recruiter call was the most pleasant part. She came across as genuinely invested in whether I succeeded, and when I asked for feedback, she provided it, which I didn't expect from a recruiter. The live coding round was the core of the process. I was given a dataset and had 45 minutes to build an ML model with it. The task itself was not especially hard, but the clock is tight, and every minute spent hunting for syntax or an approach is a minute you don't have. After that came a round on ML concepts and a behavioral interview. I can't speak much to the content of those two, so prepare for them as standard rounds.
I didn't get an offer. I was screened out after the process. The main lesson I'd pass along is that the coding round rewards fluency, not research. Be comfortable with the core library workflow, and brush up on scikit-learn before going in.
Prep tip from this candidate
Get fluent with scikit-learn's model-building workflow and practice building a model from a raw dataset under a 45-minute timer without relying on search, since the candidate was screened out for leaning on Googling despite being told it was allowed.
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Topics based on recent interview experiences.
Featured question at Reddit, Inc.
Write a function to find the median amount of rainfall for the days on which it rained.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter contact that covered their background and experience. One candidate found the recruiter supportive and received feedback when they asked for it. Another reported little detailed feedback later in the process, so experiences with recruiters may vary.
Candidates report being given a dataset and asked to build a model, such as a binary classification model, followed by questions about their approach. One report cites a 45-minute limit. Another says the interviewer moved on to systems design after the candidate finished early. Fluency with scikit-learn is likely to help.
Two candidates mention a round on ML concepts. One reports questions on tradeoffs such as logistic regression versus random forests. Prepare to explain model choice and tradeoffs clearly, since the other report gives few details about this round.
One candidate describes a four-round onsite with two DSA rounds, an ML fundamentals round and a hiring manager behavioral round. They found the DSA problems harder to parse than typical LeetCode and advised asking clarifying questions early. Other candidates do not confirm this exact format.
