Roblox·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

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.

Questions Asked (1)

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.

Algorithms & Data StructuresSystem Design

Author's notes

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

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AI HintsAI Generated

Suggested Approach

Use a deque to store hits as (timestamp, count) pairs, merging hits with the same timestamp. On query, evict all entries older than the window from the front, then return the sum of counts in the deque. This ensures both operations are amortized O(1) and queries don't scale with total hits.

Pro tip: Mention that this design is essentially a sliding window counter and can be extended to distributed settings using sharding and aggregation, which is relevant for large-scale systems like Roblox.

1. Clarify requirements and constraints

Confirm that timestamps are strictly increasing, multiple hits can share the same timestamp, and the window is fixed at 300 seconds. Ask about expected scale and whether concurrency is a concern.

2. Choose the right data structure

Select a deque (double-ended queue) to store hits in chronological order, allowing efficient removal of expired hits from the front and addition of new hits to the back.

3. Optimize storage with aggregation

Merge hits with the same timestamp into a single entry with a count to reduce memory usage and speed up queries.

4. Implement record and query methods

For record(timestamp), append or update the last entry if timestamps match. For query(timestamp), evict entries older than timestamp - 300, then return the sum of counts in the deque.

5. Analyze complexity and edge cases

Explain that both operations are amortized O(1) because each hit is added and removed at most once. Discuss edge cases like empty deque, hits exactly at the boundary, and large bursts.

Key Points to Mention

Use of deque for O(1) append and popleft operations
Aggregating hits with the same timestamp to save space
Amortized O(1) time complexity for both record and query
Query does not scale with total hits because expired hits are removed
Handling of boundary conditions (e.g., hits exactly 300 seconds old)
Potential extensions for distributed systems (sharding, aggregation)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.