Verkada Inc.·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Verkada phone screen for a software engineer role, pretty much one meaty algorithmic problem the whole time. The question was a follow-up to an earlier camera alert problem, so it built on assumed context which threw me a little.

Questions Asked (1)

Given alert intervals from multiple surveillance cameras, where each camera produces a sorted, non-overlapping list of intervals, merge all intervals across all cameras into a single sorted list of non-overlapping intervals representing any time range where at least one camera was in alert state. Aim for an O(N log k) solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is basically interval merging but across k sorted lists instead of one flat list.

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AI HintsAI Generated

Suggested Approach

Use a min-heap to merge intervals from k sorted lists, similar to merging k sorted arrays, but with interval merging logic. Pop the interval with the smallest start time, then merge it with the last interval in the result if they overlap; otherwise, append it. Push the next interval from the same camera into the heap, ensuring O(N log k) time where N is total intervals.

Pro tip: Clarify that intervals from the same camera are non-overlapping and sorted, so you only need to compare the current interval with the last merged interval; also mention that if intervals are inclusive/exclusive, adjust the overlap condition accordingly.

1. Understand the problem and constraints

Restate the problem: merge intervals from k sorted lists into one sorted, non-overlapping list. Confirm that intervals are sorted and non-overlapping within each camera, and that N is total intervals.

2. Choose the right data structure

Select a min-heap (priority queue) to efficiently retrieve the interval with the smallest start time across all cameras. Each heap entry stores the interval and the camera index (and possibly the index within that camera's list).

3. Initialize and process the heap

Push the first interval from each camera into the heap. While the heap is not empty, pop the smallest interval, merge it with the last interval in the result if they overlap, otherwise append it. Then push the next interval from the same camera if available.

4. Analyze time and space complexity

Explain that each interval is pushed and popped once, and heap operations take O(log k), leading to O(N log k) time. Space is O(k) for the heap plus O(N) for the output.

5. Discuss edge cases and trade-offs

Consider edge cases: empty input, intervals that touch (e.g., [1,2] and [2,3]), and intervals that are completely contained. Mention alternative approaches like divide-and-conquer or sweep line, and why heap is optimal here.

Key Points to Mention

Min-heap (priority queue) to efficiently merge k sorted lists
Overlap condition: next.start <= current.end (or < if intervals are half-open)
Time complexity O(N log k) and space complexity O(k) for heap
Handling edge cases: empty lists, touching intervals, contained intervals
Comparison with alternative approaches (e.g., divide-and-conquer, sweep line)
Importance of maintaining sorted order and non-overlapping property in result

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