eBay·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

eBay coding round with a list partitioning problem that sounds straightforward until you actually try to implement it cleanly. The merging step at the end tripped me up more than I expected.

Questions Asked (1)

Given a list of numbers, split them into two sublists by comparing each new number against existing elements: if any existing number is greater than the new one, add it to that list; otherwise add it to whichever list is shorter. Then merge both lists at the end.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I read the rules twice and still coded the wrong condition first.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem statement and edge cases, then walk through a concrete example to illustrate the splitting logic. Next, discuss the algorithm's time and space complexity, and finally, propose optimizations or alternative approaches, highlighting trade-offs.

Pro tip: Demonstrate awareness of real-world constraints by discussing how the algorithm would scale with large datasets and whether the merging step could be optimized or parallelized.

1. Clarify the problem

Ask questions to resolve ambiguities: What does 'any existing number is greater' mean exactly? Should we compare against all elements in both lists or just one? What if multiple lists satisfy the condition? How should ties be broken?

2. Walk through an example

Choose a small list of numbers and manually simulate the process, showing how each number is assigned to a sublist based on the rules. This demonstrates understanding and reveals edge cases.

3. Analyze complexity

Determine the time and space complexity of the naive approach. For each new number, checking against existing elements could be O(n) per element, leading to O(n^2) overall. Merging two lists is O(n). Discuss if this is acceptable.

4. Propose optimizations

Suggest improvements, such as maintaining the maximum of each list to quickly check the condition, or using a balanced binary search tree to reduce lookup time. Discuss trade-offs between simplicity and efficiency.

5. Discuss edge cases and extensions

Cover edge cases like empty input, single element, all equal elements, and negative numbers. Also consider if the algorithm can be extended to more than two lists or if the merging step can be done in-place.

Key Points to Mention

Time complexity analysis: naive O(n^2) vs optimized O(n log n) with appropriate data structures
Space complexity: O(n) for storing the two sublists and merged result
Stability of the algorithm: does the order of elements matter?
Trade-offs between different data structures (e.g., arrays vs linked lists vs trees) for maintaining lists
Real-world scalability: how the algorithm performs with large datasets and potential for parallelization
Edge cases: empty list, single element, duplicates, and negative numbers

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