Grammarly·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Grammarly coding screen, one algorithm question, pretty standard stuff. The problem itself wasn't brutal but I second-guessed my approach halfway through and it cost me some time.

Questions Asked (1)

Given a list of intervals, merge all overlapping ones and return the resulting non-overlapping set.

Algorithms & Data Structures

Author's notes

I knew the sort-then-sweep approach but fumbled explaining why sorting by start is necessary before you can safely merge.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: confirm interval representation (inclusive/exclusive, sorted input), expected output format, and edge cases. Then propose a sort-based approach: sort intervals by start time, iterate and merge overlapping intervals by comparing current start with previous end. Analyze time and space complexity, and discuss potential optimizations or alternative approaches if needed.

Pro tip: Mention that sorting is key to achieving O(n log n) time, and that you can merge in-place if the input is mutable to save space. Also, proactively discuss how to handle edge cases like empty input or intervals that just touch (e.g., [1,2] and [2,3]) to show attention to detail.

1. Clarify requirements and edge cases

Ask about interval inclusivity, input sorting, output format, and constraints. Discuss edge cases: empty list, single interval, intervals that touch, and large input.

2. Outline the algorithm

Explain that sorting by start time allows linear merging. Describe the merge condition: if current interval's start <= last merged interval's end, merge by updating the end to max of both ends.

3. Walk through an example

Use a small example like [[1,3],[2,6],[8,10],[15,18]] to demonstrate the algorithm step by step, showing how intervals are merged.

4. Analyze complexity and discuss trade-offs

State time complexity O(n log n) due to sorting, space O(n) for output (or O(1) extra if in-place). Mention that if input is already sorted, time is O(n).

5. Implement and test

Write clean code with meaningful variable names. Test with edge cases and verify correctness. If time permits, discuss alternative approaches like using a stack or sweep line.

Key Points to Mention

Sorting intervals by start time is crucial for efficient merging.
Merge condition: if current start <= last end, update last end to max(last end, current end).
Time complexity: O(n log n) due to sorting; space complexity: O(n) for output.
Edge cases: empty input, single interval, intervals that touch (e.g., [1,2] and [2,3]), and unsorted input.
In-place merging possible if input can be modified, reducing extra space.
Alternative approaches: sweep line algorithm or using a stack, but sorting is simplest.

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