Updated by Reddit candidates
Staff Machine Learning Engineer Interview Experience
I interviewed at maybe 15 or 20 places in six months, and for every onsite I got an offer, but Reddit still threw me for a loop because the ML system design leaned way more infra than modeling.
Interview process
I interviewed for a Senior Staff MLE role, and the biggest thing I’d say is Reddit felt pretty team dependent, so I wouldn’t assume every team runs the exact same loop. Mine started with a recruiter screen that surprisingly included a couple very basic ML questions, then a hands-on technical screen where I had to work with data in a notebook-style environment and get a model actually running. The final loop was ML fundamentals, ML system design, coding, and a hiring manager round. The ML system design round was the most interesting because I had prepped for a modeling-heavy conversation and got pushed much more into ML infra, like deployment, latency, feature pipelines, and how the A/B test would work under the hood.
- Recruiter screen
- Technical interview
- Final round
Interview tips
Ask the recruiter very clearly what kind of MLE loop you’re getting, because modeling-heavy and infra-heavy prep are not the same at all. For the tech screen, practice Kaggle-style problems in a notebook where you have to inspect messy data, clean it up, pick something reasonable, and get a model working under time pressure. For fundamentals, treat it like cramming for an ML final: know missing data, feature scaling, overfitting and underfitting, L1 vs L2, and deep learning basics like vanishing or exploding gradients, and if you forgot the math, go do enough of it by hand that it sticks again. For system design, have a repeatable framework for ranking, fraud, and regression problems, and don’t stop at the model. Be ready to talk about labels, features, deployment, latency, monitoring, rollback, and A/B testing. I also felt like interviewers were trying to map my actual experience to their team’s work, especially in the hiring manager round, instead of only asking generic behavioral questions. And honestly, in this market the bar just feels higher everywhere. I don’t think you can get away with only having good thought process anymore. You usually need the thing to actually work too.
Company culture
From my experience, it felt like Reddit is pretty team dependent, much more like Amazon than the old one-size-fits-all Google or Facebook loop. The whole process felt practical to me, closer to Stripe, and a lot of the variation seemed to come from the specific team and the interviewer’s own background.
Questions asked
Overview
My final loop had the usual senior/staff MLE set: fundamentals, ML system design, coding, and a hiring manager round. The most distinctive part was that my system design interviewer pushed the discussion much more toward ML infra than model choice.
Question types asked
Specific questions asked
How would you deploy the model?
How would you make sure latency is not a problem?
How would you gather labels and features, and what would the pipeline look like?
How would the A/B test work under the hood?
Would you deploy to all hosts at once or roll it out gradually?
I started the way I usually do for ranking problems: define the goal, then lay out user features, contextual features, and item features. I talked about things like age, gender, demographics, time of day, seasonality, and metadata about the content. I expected more discussion around model choice, but this interviewer was clearly more infra-oriented and kept pushing into serving, latency, feature and label pipelines, deployment strategy, monitoring, rollback, and how the A/B test would really work. That threw me a little because I’m more modeling than infra.
I treated those as table stakes and answered them in a practical way, because they come up constantly in real data. I walked through how I think about missing data, badly scaled features, outliers, and debugging overfitting versus underfitting. What makes this round hard is that it can start from those common scenarios and then keep drilling into deeper statistics or math depending on the interviewer.
Why does L1 tend to push some features to zero?
I knew the practical distinction, but this is exactly the kind of question where the interviewer can keep going deeper than the textbook definition. My experience with these fundamentals rounds is that knowing the high-level answer is not enough. You need enough of the math fresh in your head that you can explain why L1 behaves the way it does and not just say that it does. That’s what makes this round feel like cramming for an ML final.
Solve this coding problem and make it compile and pass the test cases.
This was not a pseudo-code round. I had to write something that actually ran, compiled, and passed tests, which I honestly think is harder than the older style where you just talk through code. My impression was that they still expected solid basics like trees, hash maps, sorting, and maybe some greedy thinking, but the problem itself felt more practical than a pure algorithm trick question.
You mentioned a project on your resume. Can you go deeper on what you actually did there?
This felt more team specific than the generic behavioral loops I remember from places like Google or Facebook. I mostly walked through the projects most relevant to the team, what I actually owned, and why that background matched what they were hiring for. It was less about canned behavioral prompts and more about whether my experience lined up with the real work of that team.
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