Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Bloomberg SWE interview with a bucketing/classification problem that looks deceptively clean on paper but has a bunch of edge cases once you start coding. The binary search path felt natural to me but they pushed on the linear merge approach too, which I hadn't thought through carefully beforehand.

Questions Asked (1)

Given a sorted boundaries array and an input array, classify each element into the bucket it falls into based on consecutive boundary pairs. Return the bucket index for each element or the grouped buckets, and discuss both an O(n log k) binary search approach and an O(n + k) merge-style approach.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with binary search since the boundaries are sorted, felt pretty solid there.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and expected output format, then present both the binary search and merge-style approaches with their time/space complexities and trade-offs. Emphasize that the merge-style approach is optimal when the input array is also sorted, while binary search is more general and simpler to implement.

Pro tip: Mention that in practice, the merge-style approach can be implemented with a single pass using two pointers, and that binary search can be optimized by reusing the previous bucket index as the lower bound for the next search, reducing the effective search space.

1. Clarify requirements and edge cases

Ask whether the input array is sorted, what to return (indices or grouped buckets), and how to handle elements outside the boundary range. Confirm boundary inclusivity (e.g., [low, high) or (low, high]).

2. Explain the binary search approach

For each element, perform binary search on the boundaries to find the bucket index. Discuss time complexity O(n log k) and space O(1) or O(n) for output.

3. Explain the merge-style approach

If the input array is sorted, use two pointers to traverse both arrays simultaneously, assigning each element to its bucket in O(n + k) time. If not sorted, sort the input first (O(n log n)) or use counting sort if range is small.

4. Compare trade-offs and choose

Discuss when each approach is preferable: binary search for unsorted input or when k is small; merge-style for sorted input or when n is large and k is comparable. Mention that merge-style can also group elements directly.

5. Provide code or pseudocode

Outline the implementation for both approaches, highlighting key details like boundary conditions and pointer updates. If time permits, write clean code for one approach.

Key Points to Mention

Time complexity: O(n log k) for binary search, O(n + k) for merge-style (assuming sorted input).
Space complexity: O(1) extra space for binary search (excluding output), O(k) for grouped buckets in merge-style.
Boundary conditions: how to handle elements equal to boundaries, elements below the first boundary, and above the last boundary.
Optimization: in binary search, start the search from the previous bucket index to reduce comparisons.
Use cases: binary search is more general; merge-style is optimal for sorted input and can produce grouped output directly.
Edge cases: empty boundaries, empty input, single boundary, duplicate boundaries.

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