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Mavenclinic·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Interviewed for a software engineer role at Mavenclinic and got a scheduling/interval problem with a twist I didn't fully anticipate. Pretty straightforward coding round but the break_time wrinkle added enough complexity to make me second-guess my initial approach.

Questions Asked (1)

Q1

Given a list of appointment intervals in HHMM format and a mandatory minimum break time between consecutive appointments, write a function that returns true if a person can attend all appointments without overlap or insufficient gaps.

Algorithms & Data Structures
Author's notes

My first instinct was to sort by start time and check for overlaps, which is the standard interval problem.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input format and constraints, then propose sorting the intervals by start time and checking for overlaps or insufficient gaps between consecutive appointments. Convert HHMM strings to minutes for easy comparison, and iterate through the sorted list to validate the break requirement.

Pro tip: Mention that sorting is O(n log n) and that you can optimize by checking only adjacent intervals after sorting, which is optimal for this problem. Also, discuss edge cases like empty list or single appointment to show thoroughness.

1. Clarify requirements

Ask about input format (e.g., list of strings or tuples), whether intervals are inclusive, and if the break time is in minutes. Confirm that appointments are given in HHMM format and that the break is mandatory between consecutive appointments.

2. Convert and sort

Convert each HHMM string to minutes since midnight (e.g., '0930' -> 570). Sort the intervals by start time to process them in chronological order.

3. Check overlaps and gaps

Iterate through the sorted intervals, comparing the end time of the previous appointment plus the break time with the start time of the current appointment. If the previous end + break > current start, return false.

4. Handle edge cases

If the list is empty or has one appointment, return true. Also consider appointments that span midnight (though likely not needed) and ensure break time is non-negative.

5. Analyze complexity

State that the time complexity is O(n log n) due to sorting, and space complexity is O(1) if sorting in place or O(n) if creating a new list. This is optimal for comparison-based sorting.

Key Points to Mention

  • Sorting intervals by start time is key to simplifying the overlap check.
  • Converting HHMM to minutes since midnight avoids string manipulation and simplifies arithmetic.
  • The break time must be added to the end of the previous appointment before comparing with the next start.
  • Edge cases: empty list, single appointment, back-to-back appointments with exactly the break time.
  • Time complexity: O(n log n) due to sorting; space complexity: O(1) or O(n) depending on implementation.
  • Clarify if intervals are inclusive/exclusive and if break time is in minutes or HHMM format.

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