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Roblox·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026Remote

Summary

Roblox software engineer round, one coding question the whole time. The problem looked like a frequency counter at first glance but the tie-breaking rules added enough wrinkles to slow me down.

Questions Asked (1)

Q1

You're given a list of call stack snapshots from a sampling profiler, where each snapshot is an ordered list of function names from outermost to innermost caller. Find the call stack that appears most frequently. If there's a tie, prefer the deeper stack, and if still tied, prefer the lexicographically smaller one. Then extend your solution to handle multiple threads, returning the most frequent stack per thread.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The base case came together fast, just convert each stack to a tuple, count with a dict, then sort or reduce with the tie-break logic.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a hash map to count stack frequencies, using a custom comparator for tie-breaking. For multiple threads, group snapshots by thread ID and apply the same logic per thread, discussing trade-offs like memory usage and parallelization.

Pro tip: Demonstrate awareness of real-world profiler data: stacks can be deep and numerous, so consider memory and time complexity; also, mention that tie-breaking rules reflect practical needs like prioritizing deeper stacks for more specific hotspots.

1. Clarify requirements and edge cases

Ask about input size, thread ID representation, and whether stacks are guaranteed non-empty. Confirm tie-breaking rules and output format.

2. Design single-thread solution

Use a hash map to count occurrences of each stack (as a tuple or string). Track the max frequency and apply tie-breakers: deeper stack first, then lexicographically smaller.

3. Extend to multiple threads

Group snapshots by thread ID, then run the single-thread algorithm per group. Discuss whether to process threads sequentially or in parallel.

4. Analyze complexity and trade-offs

Time: O(N * L) where N is number of snapshots and L average stack depth; space: O(U * L) for U unique stacks. Mention potential optimizations like hashing stacks or using tries.

5. Test with examples

Walk through a small example, including ties, to verify correctness. Consider edge cases like empty input or single snapshot.

Key Points to Mention

  • Hash map for frequency counting with stack as key (e.g., tuple or string)
  • Custom comparator for tie-breaking: depth then lexicographic order
  • Grouping by thread ID for multi-threaded extension
  • Time and space complexity analysis
  • Potential optimizations: parallel processing, memory-efficient stack representation
  • Real-world relevance: identifying hotspots in profiler data

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