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Roblox·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Roblox SWE interview that built on a previous call-stack parsing problem, adding two progressively harder variants back to back. Pretty focused session, no fluff.

Questions Asked (2)

Q1

Given a trace of enter/exit events representing function calls, find the longest call stack depth observed at any point during execution.

Algorithms & Data Structures
Author's notes

This is the follow-up to the basic call-stack parser, so you're expected to already have the core structure in your head.

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AI HintsAI Generated

Suggested Approach

Clarify the input format (e.g., list of strings like 'enter f' and 'exit f') and define depth as the number of active calls. Then iterate through the trace, incrementing a counter on enter and decrementing on exit, tracking the maximum value seen. This yields an O(n) time, O(1) space solution.

Pro tip: Mention that you can validate the trace by checking that depth never goes negative and ends at zero, which shows you think about robustness and edge cases.

1. Clarify input and output

Ask about the trace format (e.g., list of strings, events with function names) and confirm that depth is the number of currently active function calls. Ensure you know what to return (maximum depth).

2. Define depth tracking

Initialize a current depth counter to 0 and a max depth variable to 0. Explain that each 'enter' increases depth by 1 and each 'exit' decreases it by 1.

3. Iterate and update

Loop through each event in order. On 'enter', increment current depth and update max if current > max. On 'exit', decrement current depth.

4. Handle edge cases

Consider empty trace (return 0), unbalanced traces (depth goes negative or doesn't end at 0), and nested calls. Mention that you can optionally validate the trace.

5. Analyze complexity

State that the algorithm runs in O(n) time and O(1) extra space, where n is the number of events. This is optimal since you must read each event at least once.

Key Points to Mention

  • Use a single pass with a counter for current depth and a variable for maximum depth.
  • Time complexity O(n) and space complexity O(1).
  • Edge cases: empty trace, unbalanced enter/exit, and traces that don't end at depth 0.
  • The trace can be validated by ensuring depth never goes negative and ends at 0.
  • If the trace is very large, the algorithm is efficient and can be streamed.
  • Clarify assumptions about input format and whether function names matter (they don't for depth).

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

Q2

Extend the same enter/exit event trace to a multi-threaded scenario where each event includes a thread ID. Return the maximum call stack for each thread independently.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one tripped me up for a second.

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AI HintsAI Generated

Suggested Approach

Use a hash map to maintain a separate stack depth counter for each thread ID, updating on enter and exit events. Track the maximum depth per thread as you process events, then return the maximums. This approach is O(n) time and O(t) space, where t is the number of threads.

Pro tip: Mention that you assume the event trace is well-formed (no unmatched exits) and discuss how you would handle invalid traces, such as ignoring exits when the stack is empty or logging errors. This shows attention to edge cases and production readiness.

1. Clarify assumptions and edge cases

Confirm that each event has a thread ID and type (enter/exit), and that the trace is per-thread consistent. Ask about handling malformed traces (e.g., exit without enter) and whether thread IDs are bounded.

2. Choose data structures

Use a hash map (dictionary) to map thread ID to its current stack depth, and another hash map to store the maximum depth seen per thread. This allows O(1) updates per event.

3. Process events sequentially

Iterate through the event trace. For each event, update the current depth for that thread: increment on enter, decrement on exit. After each update, compare and update the maximum depth for that thread.

4. Handle invalid events

If an exit event would make depth negative, either ignore it or raise an error based on requirements. Document this behavior.

5. Return results

After processing all events, return a mapping from thread ID to its maximum stack depth. If a thread never appeared, it may be omitted or included with depth 0, depending on requirements.

Key Points to Mention

  • Time complexity O(n) and space complexity O(t) where n is number of events and t is number of threads.
  • Using a hash map for per-thread state avoids interference between threads.
  • Tracking maximum depth incrementally avoids storing full stacks, saving memory.
  • Handling edge cases like unmatched exits or threads with no events.
  • The solution is single-pass and can handle streaming data.
  • Thread IDs can be any hashable type; no assumption about ordering.

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