Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Bloomberg SWE interview, got a linked list merging problem. Pretty standard stuff for this type of role but the k-list variant has enough edge cases to trip you up if you're not careful.

Questions Asked (1)

Given an array of k sorted linked lists, merge them all into a single sorted linked list and return its head.

Algorithms & Data Structures

Author's notes

The naive approach works but they're probably watching to see if you jump straight to a priority queue or think through it first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., k and list sizes) and discussing trade-offs between approaches. Then present a solution using a min-heap of size k to repeatedly extract the smallest node, achieving O(N log k) time and O(k) space. Walk through the algorithm with a small example and analyze complexity.

Pro tip: Mention that for small k, a divide-and-conquer merge might be more cache-friendly and avoid heap overhead, showing you consider practical performance beyond big-O.

1. Clarify constraints and edge cases

Ask about the range of k, list lengths, memory limits, and whether lists can be empty. Discuss edge cases like k=0 or all lists empty.

2. Discuss possible approaches

Compare naive sequential merging (O(kN)), divide-and-conquer (O(N log k)), and heap-based (O(N log k)). Explain trade-offs in time, space, and implementation complexity.

3. Present the heap-based algorithm

Describe initializing a min-heap with the head of each non-empty list, then repeatedly pop the smallest node, append it to the result, and push its next node if it exists.

4. Analyze complexity and optimize

State time O(N log k) and space O(k) for the heap. Mention that for very small k, a simpler merge might be faster due to lower constant factors.

5. Test with examples

Walk through a small example (e.g., k=3 lists) to verify correctness, and consider edge cases like one list empty or all lists of length 1.

Key Points to Mention

Time complexity O(N log k) where N is total nodes and k is number of lists
Space complexity O(k) for the min-heap
Use of a min-heap (priority queue) to efficiently select the smallest current node
Handling of empty lists and edge cases (k=0, all lists empty)
Alternative divide-and-conquer approach with same complexity but different constants
Stability of merge (preserving order of equal elements) if required

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