Roblox·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

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.

Questions Asked (1)

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.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

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

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (input size, memory limits, tie-breaking rules) and then present a hash map solution for the basic case, followed by a heap-based approach for top-k and a streaming solution using count-min sketch or distributed counters. Discuss trade-offs between exact and approximate methods, and how to handle ties deterministically.

Pro tip: Demonstrate awareness of real-world log data characteristics (skewed distributions, high cardinality) and propose a hybrid approach that uses exact counting for frequent items and approximate counting for the long tail, which shows both algorithmic and system design maturity.

1. Clarify requirements and constraints

Ask about input size, memory limits, tie-breaking rules, and whether the function should return top-k or just the top-1. Also clarify if the log is static or streaming.

2. Propose a baseline solution

Use a hash map to count frequencies in O(n) time and O(m) space, where m is the number of unique calls. Then find the max by iterating over the map.

3. Extend to top-k and ties

For top-k, use a min-heap of size k to keep the k most frequent calls, or use bucket sort if frequencies are bounded. For ties, define a deterministic rule (e.g., lexicographical order) and apply it.

4. Address streaming and large-scale data

For streaming, use algorithms like Misra-Gries or count-min sketch to approximate frequencies with bounded memory. For distributed logs, use map-reduce or sharded counters with a merge step.

5. Discuss trade-offs and optimizations

Compare exact vs. approximate methods, memory vs. accuracy, and latency vs. throughput. Mention potential optimizations like using a trie for prefix aggregation or sampling for very high-volume streams.

Key Points to Mention

Time and space complexity of the hash map approach: O(n) time, O(m) space.
Using a min-heap for top-k: O(n log k) time, O(k) space.
Tie-breaking strategies: lexicographical order, first occurrence, or random.
Streaming algorithms: Misra-Gries, count-min sketch, or lossy counting.
Distributed processing: map-reduce, sharding, and merging partial counts.
Real-world considerations: skewed distributions, high cardinality, and memory constraints.

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