Atlassian·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Apr 2026

Summary

Atlassian coding round, pretty much a pure algorithms session. The whole thing centered on interval problems and I spent most of my prep time on trees so that was fun.

Questions Asked (1)

Given a list of intervals each with a start and end time, solve a problem involving overlapping intervals. This could mean merging overlapping intervals, counting the maximum number that overlap at any single point, or finding the minimum number of meeting rooms needed to schedule them all without conflict.

Algorithms & Data Structures

Author's notes

I knew the sort-by-start trick going in but fumbled when they asked me to extend it to the meeting rooms variant.

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AI HintsAI Generated

Suggested Approach

First, clarify which specific problem the interviewer wants (merging, max overlap, or min rooms) and confirm edge cases. Then, propose a solution using sorting and either a heap or sweep line, explaining the time and space complexity. If time permits, discuss alternative approaches and trade-offs.

Pro tip: Always start by asking clarifying questions about input constraints (e.g., interval inclusivity, empty list, large input) to show attention to detail. Mention that sorting by start time is often the first step, but for min rooms, a heap of end times is key.

1. Clarify the problem

Ask which variant: merging, max overlap, or min rooms. Confirm interval inclusivity, input size, and expected output.

2. Choose the right algorithm

For merging: sort by start and merge. For max overlap: sweep line with events. For min rooms: sort starts and ends, use two pointers or min-heap.

3. Walk through an example

Trace the algorithm on a small example to verify correctness and handle edge cases like touching intervals.

4. Analyze complexity

State time and space complexity. Usually O(n log n) time due to sorting, O(n) space for output or heap.

5. Discuss trade-offs and optimizations

Mention alternative approaches (e.g., difference array for bounded times) and when they might be preferable.

Key Points to Mention

Sorting intervals by start time as a common first step
Using a min-heap to track end times for minimum meeting rooms
Sweep line algorithm with events for maximum overlap
Handling edge cases: empty input, single interval, intervals that just touch
Time complexity: O(n log n) due to sorting, space complexity: O(n)
Difference array technique for bounded integer times

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