Roblox Machine Learning Engineer interview questions

17 Machine Learning Engineers have shared notes from their Roblox interviews, with the questions they were asked in each round. That is 12% of the 142 Roblox interview notes on Screna AI.

17 Machine Learning Engineer interview notes

Roblox·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Roblox ML engineer interview that went deep on recommendation systems. One long design question covering basically the entire ML lifecycle, from raw features to live A/B tests. Not a casual screen.
  • Design a game recommendation system for users. Cover the full ML pipeline: feature engineering, candidate generation, model selection, training data construction, loss functions, optimizers, offline metrics, and online experimentation.

“This is a lot to cover in one question and I definitely didn't pace myself well.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Got a coding question from Roblox for an ML Engineer role that was basically a topological sort with a specific deterministic leveling rule. The problem was well-defined but the edge cases around missing deps and cycles needed careful handling.
  • Given a list of component dependency declarations, return a valid build order where each component appears only after all its dependencies. The order must follow a deterministic wave-based leveling rule: at each step, all components whose dependencies are satisfied get emitted together, sorted by their original declaration index. Return an error if any dependency is undeclared or if there's a cycle.

“The core of it is Kahn's algorithm but the twist is you can't just do a standard BFS and call it done.”

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

Jun 2026
ML system design round at Roblox for an MLE role. The question was a full end-to-end design problem around game genre classification, which sounds contained until you realize how many rabbit holes it opens up. Left feeling like I covered maybe 60% of what they were probably looking for.
  • Design a complete ML system from scratch that classifies games into genres. Walk through problem framing, data sourcing, modeling approach, training, evaluation, serving, and how the system evolves over time.
  • How would you handle class imbalance for niche genres, and what evaluation metrics make sense here?
  • How do you structure the feedback loop once the system is live? Specifically, how do editorial review and user-applied tags feed back into the model.
  • Walk me through the iteration roadmap. Where do you start and what does the path to a full multimodal system look like?

“This is the kind of question where you can spend 20 minutes just on taxonomy and never get to modeling.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Roblox ML engineer interview had a system design coding problem that felt more backend-y than I expected for the role. The question was clean but the follow-up constraints are where things got interesting.
  • Design an in-memory hit counter that tracks how many requests occurred in the last 5 minutes. Implement two methods: one to record a hit at a given timestamp, and one to return the count of hits in the past 300 seconds relative to a given timestamp. Timestamps are strictly increasing but multiple hits can share the same timestamp. Optimize so that queries don't scale with total hits ever recorded.

“My first instinct was a queue and I said so out loud, which was fine for the basic case.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Roblox ML Engineer interview with a sliding window coding problem. Pretty focused session, just the one algorithmic question from what I can tell, and the constraint to hit O(n) was the whole point of the exercise.
  • Given a list of integers, a target value, and a fixed window size k, find all contiguous windows of size k that contain at least one occurrence of the target. Return the start and end indices of each such window. You need to do this in O(n) time using a counter to track how many times the target appears in the current window.

“The O(n) constraint is where this gets interesting.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Coding screen for an MLE role at Roblox, pretty standard stuff centered around log processing. The question was straightforward but the follow-up directions they hinted at made it clear they wanted to see if you could think beyond the naive solution.
  • Given a sequence of application log entries (each being a function or API call name), write a function that returns the most frequently occurring call along with its count. Be prepared to discuss handling ties, returning the top-k most frequent calls, and scaling to streaming or large-scale log data.

“Nailed the basic part fast, just a hashmap frequency count and a linear scan for the max.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Roblox ML engineer screen that turned into a pretty deep systems exercise. Started simple but they kept adding constraints until I was basically designing a streaming frequency tracker from scratch.
  • Given a list of log entries where each entry contains a function or call name (and possibly a timestamp and other fields), find the most frequently occurring function call. Be prepared to extend this to top-k results, time-windowed queries, streaming inputs, tie-breaking, and call-stack or caller-callee pair analysis.

“I started with the obvious hashmap approach and felt pretty good about it.”

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

Jun 2026
Roblox ML Engineer interview centered on a full system design for a recommendation engine. The scope was pretty wide and they clearly wanted to see how you'd hold the whole thing together end to end, not just rattle off buzzwords.
  • Design a complete recommendation system from scratch for a product of your choice, covering candidate generation, ranking, re-ranking, and serving architecture.
  • How would you handle latency requirements at the p99 level for a recommendation system serving millions of users?
  • How do you approach cold start for both new users and new items in a recommendation system?
  • Walk me through how you'd evaluate a recommendation model both offline and online, and how you'd reconcile when they disagree.
  • What are the trade-offs between model freshness and serving cost in a production recommendation system?

“This was basically the whole interview.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

May 2026
Did a system design round for an MLE role at Roblox, and the whole session basically revolved around one problem that seemed simple but had a lot of layers once you started pulling on it. Came away feeling okay about it but not great.
  • Design a hit counter that tracks the number of hits received in the past 5 minutes. The counter accepts timestamps in seconds, calls arrive in non-decreasing timestamp order, and multiple hits can share the same timestamp. Walk through a deque-based solution and extend it to support per-user rate limiting using a dictionary of deques.

“I started with a naive array approach and they let me dig myself into a corner before asking about memory.”

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Roblox·Machine Learning Engineer·Technical Phone Screen

May 2026
Went through a project presentation round for an MLE role at Roblox and it was kind of a weird experience. I kept pausing between slides to invite questions and got nothing, just silence. The only thing the interviewer asked at the very end was a token question about my role, which felt more like a formality than actual curiosity.
  • What was your specific role and contribution in the project you presented?

“Asked at the very end, clearly as an afterthought.”

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