Scale.ai·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Scale.ai coding round for a software engineer role. One problem, simulation-style, not too bad on the surface but there's a bit of nuance in how you track state over time.

Questions Asked (1)

You're given a list of party events, each with a timestamp string in HH:MM format and a type of either ARRIVAL or DEPARTURE. Compute the attendance count at each hour of the day and return the peak attendance across all hours.

Algorithms & Data Structures

Author's notes

My first instinct was to overcomplicate this with intervals.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then use a difference array or sweep line approach to compute attendance per hour in O(n + 24) time. Convert each timestamp to an hour index, apply +1 for arrivals and -1 for departures at that hour, then prefix sum to get attendance per hour and track the maximum.

Pro tip: Mention that you would handle departures before arrivals within the same hour if the problem implies that a departure at HH:MM means the person leaves at the start of that hour, or clarify the exact semantics with the interviewer to avoid off-by-one errors.

1. Clarify requirements and edge cases

Ask whether timestamps are inclusive, how to handle multiple events at the same time, and whether attendance should be computed at the start or end of each hour. Confirm the output format (e.g., peak count only or also the hour).

2. Choose an efficient algorithm

Use a difference array of size 25 (hours 0-23 plus a sentinel) to record net changes per hour. Alternatively, sort events and sweep, but the difference array is simpler and O(n + 24).

3. Process events and compute attendance

For each event, parse the hour from HH:MM, then increment the difference array for ARRIVAL and decrement for DEPARTURE at that hour. After processing all events, compute prefix sums to get attendance at each hour.

4. Find and return the peak

Iterate through the attendance array to find the maximum value. If needed, also track the hour(s) where the peak occurs. Return the peak attendance count.

5. Test with examples and edge cases

Walk through a small example, including cases with no events, all arrivals, all departures, and events at the same hour. Verify that the peak is computed correctly.

Key Points to Mention

Time complexity: O(n + 24) = O(n) with constant space for hours, which is optimal.
Space complexity: O(1) since the hour array size is fixed (24 or 25).
Handling of timestamps: parse HH:MM to extract the hour (0-23), ignoring minutes unless the problem requires finer granularity.
Difference array technique: efficient for range updates and point queries, here used for point updates and prefix sum.
Edge cases: empty list, events at midnight (00:00), multiple events at same hour, and departures before arrivals in the same hour.
Clarification on semantics: whether attendance at hour H includes events at exactly H:00, and whether departures at H:MM reduce attendance for that hour or the next.

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