Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Amazon SWE coding round, one problem the whole session. Classic interval merging but they pushed hard on complexity analysis and edge cases, which is where I spent most of my time.

Questions Asked (1)

Given a list of half-open intervals [start, end), merge all overlapping or contiguous ones and return a sorted list of non-overlapping intervals. Walk through your time and space complexity, and handle edge cases like empty input, fully nested intervals, and negative coordinates.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the core merge logic pretty fast, sort by start then sweep through.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the interval semantics (half-open, contiguous merging) and edge cases, then propose sorting by start time and merging in a single pass. Walk through the algorithm with a concrete example, analyze time and space complexity, and explicitly address the listed edge cases.

Pro tip: At Amazon, emphasize how your solution scales with large inputs and how you'd test it—mention property-based testing or randomized inputs to catch subtle bugs with negative coordinates and nested intervals.

1. Clarify requirements and edge cases

Confirm that intervals are half-open [start, end), that contiguous intervals (end == next start) should merge, and that the output must be sorted. Ask about input size, data types, and whether the input list can be modified.

2. Outline the algorithm

Sort intervals by start coordinate. Iterate through the sorted list, maintaining a current merged interval; if the next interval's start is <= current end, extend the current end to the maximum of the two ends; otherwise, append the current interval and start a new one.

3. Walk through an example

Trace the algorithm on a sample input that includes overlapping, contiguous, nested, and negative intervals to demonstrate correctness and handling of edge cases.

4. Analyze complexity

State that sorting dominates time complexity at O(n log n), and the merge pass is O(n), so overall O(n log n). Space complexity is O(n) for the output (or O(log n) to O(n) for sorting depending on implementation).

5. Discuss edge cases and testing

Explicitly address empty input (return empty list), single interval, fully nested intervals, negative coordinates, and intervals that touch at endpoints. Mention how you would test these cases.

Key Points to Mention

Sorting by start time is key to enabling a linear merge pass.
Half-open intervals mean [1,2) and [2,3) are contiguous and should merge.
Time complexity: O(n log n) due to sorting; space complexity: O(n) for output.
Edge cases: empty input, single interval, nested intervals, negative coordinates, and intervals that touch.
Use max(current_end, next_end) when merging to handle nested intervals correctly.
The algorithm is stable and can be implemented in-place if the input can be modified.

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