Openai·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Interviewed for a software engineering role at OpenAI and got hit with the merge intervals problem. Pretty standard algorithmic coding round, nothing too wild, but the pressure of the company name definitely made my brain move slower than usual.

Questions Asked (1)

Given a list of intervals, merge all overlapping ones and return a list of non-overlapping intervals covering the full input range.

Algorithms & Data Structures

Author's notes

Knew this problem but still fumbled the sorting step at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., whether intervals are inclusive, input size, sortedness). Then propose sorting intervals by start time and iterating through them to merge overlapping ones, explaining the logic and complexity. Finally, discuss edge cases and potential optimizations.

Pro tip: Mention that sorting is the key to achieving O(n log n) time, and that without sorting, the problem is more complex. Also, proactively discuss how you would handle large inputs or streaming data, showing awareness of scalability.

1. Clarify requirements and edge cases

Ask about interval inclusivity, input size, whether intervals are sorted, and expected output format. Discuss edge cases like empty input, single interval, and intervals that just touch.

2. Outline the algorithm

Explain that you will sort intervals by start time, then iterate and merge if the current interval overlaps with the last merged interval. Otherwise, add the current interval to the result.

3. Walk through an example

Choose a small example (e.g., [[1,3],[2,6],[8,10],[15,18]]) and demonstrate step-by-step how the algorithm merges intervals, highlighting the comparison condition.

4. Analyze complexity and discuss optimizations

State that sorting takes O(n log n) and merging takes O(n), so overall O(n log n) time and O(n) space for the output. Mention that if input is already sorted, it's O(n).

5. Handle edge cases and conclude

Discuss how the algorithm handles edge cases, and mention potential variations like merging intervals in a stream or with different data structures.

Key Points to Mention

Sorting intervals by start time is crucial for efficient merging.
Overlap condition: next.start <= current.end (or < if exclusive).
Time complexity: O(n log n) due to sorting; space complexity: O(n) for output.
Edge cases: empty input, single interval, intervals that are already non-overlapping, intervals that touch.
If input is already sorted, merging can be done in O(n) time.
Potential follow-up: how to handle large datasets or streaming intervals.

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