Squarespace·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Squarespace software engineer round with a clean algorithmic problem. Nothing too wild but it made me think more carefully than I expected about interval intersection.

Questions Asked (1)

Given an integer array and a list of unsorted intervals, count how many elements in the array fall within every single interval (inclusive bounds).

Algorithms & Data Structures

Author's notes

The core move is finding the intersection of all intervals first, which just means taking the max of all start values and the min of all end values.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: for each element in the array, check if it lies within every interval's inclusive bounds. Then, optimize by computing the intersection of all intervals (max of lows, min of highs) and counting array elements within that intersection, which is O(n + m) time.

Pro tip: Mention edge cases upfront, like empty intervals or arrays, and note that if the intersection is empty, the answer is zero. This shows attention to detail and prevents incorrect assumptions.

1. Clarify the problem

Confirm that intervals are inclusive and that we need elements present in all intervals. Ask about input sizes and whether intervals can be empty or invalid.

2. Brute-force approach

For each element, iterate through all intervals to check if it falls within each. This is O(n*m) time and serves as a baseline.

3. Optimize with intersection

Compute the intersection of all intervals: low = max of all interval starts, high = min of all interval ends. If low > high, the intersection is empty.

4. Count elements in intersection

Iterate through the array and count elements where low <= element <= high. This is O(n + m) time and O(1) extra space.

5. Analyze complexity and edge cases

Discuss time and space complexity, and handle edge cases like empty array, empty interval list, or invalid intervals (start > end).

Key Points to Mention

Inclusive bounds: intervals include their endpoints.
Intersection of intervals: max of starts and min of ends.
Time complexity: O(n + m) for optimized solution vs O(n*m) brute-force.
Space complexity: O(1) extra space for optimized solution.
Edge cases: empty array, empty intervals, invalid intervals, no common intersection.
Clarifying questions: input sizes, interval validity, and whether intervals can overlap.

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