Roblox·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

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.

Questions Asked (1)

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.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

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

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a deque-based sliding window solution that evicts timestamps older than 5 minutes. Extend the design to per-user rate limiting by maintaining a dictionary mapping user IDs to deques, and discuss trade-offs like memory usage and concurrency.

Pro tip: Mention that the non-decreasing timestamp order allows O(1) amortized eviction, and proactively discuss how to handle out-of-order timestamps or clock skew in production.

1. Clarify requirements and constraints

Confirm the window size (5 minutes), timestamp unit (seconds), ordering guarantee (non-decreasing), and whether per-user limits are needed. Ask about expected scale and concurrency.

2. Design the global hit counter

Use a deque to store timestamps of hits. On each hit, append the timestamp, then remove timestamps older than current_time - 300. The count is the deque size.

3. Extend to per-user rate limiting

Maintain a dictionary mapping user IDs to deques of their hit timestamps. Apply the same sliding window logic per user, and optionally enforce a max hits per user within the window.

4. Analyze complexity and trade-offs

Discuss time complexity (O(1) amortized per hit) and space complexity (O(n) where n is hits in window). Compare with alternative approaches like circular buffers or timestamp buckets.

5. Address concurrency and production concerns

Mention thread safety (locks or concurrent data structures), memory management for inactive users, and handling out-of-order timestamps or clock drift.

Key Points to Mention

Sliding window with deque for O(1) amortized operations
Eviction condition: timestamp <= current_time - 300
Per-user dictionary of deques for rate limiting
Memory cleanup for inactive users to avoid leaks
Thread safety and concurrency considerations
Trade-offs vs. fixed window or token bucket algorithms

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