← HubSpot Interview Insights

HubSpot·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

HubSpot software engineer interview that came down to a classic linked list merge problem. They wanted both iterative and recursive implementations, which I wasn't fully expecting, plus a complexity breakdown and edge case handling. Felt like a solid technical screen overall.

Questions Asked (1)

Q1

Given two sorted singly linked lists, merge them into a single sorted linked list. Implement both an iterative and a recursive solution, analyze the time and space complexity of each, and handle edge cases like empty lists and duplicate values.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The iterative version came naturally enough.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then walk through the iterative two-pointer approach with a dummy node, followed by the recursive approach. Analyze time and space complexity for both, emphasizing the trade-off between O(1) extra space for iterative and O(n+m) stack space for recursive. Finally, discuss handling duplicates and empty lists.

Pro tip: Mention that the iterative solution is generally preferred in production due to constant space, but the recursive solution showcases elegant code; also note that using a dummy node simplifies edge cases and reduces bugs.

1. Clarify requirements and edge cases

Confirm that the lists are singly linked, sorted in ascending order, and that duplicates should be preserved. Discuss edge cases: one or both lists empty, lists of different lengths, and all elements in one list smaller than the other.

2. Iterative solution with dummy node

Explain using a dummy node to simplify list construction. Maintain a current pointer and compare nodes from both lists, appending the smaller one and advancing that list. After one list is exhausted, append the remainder of the other list.

3. Recursive solution

Describe the recursive approach: if one list is empty, return the other; otherwise, compare the head nodes, set the smaller node's next to the recursive merge of the rest, and return the smaller node. Highlight base cases.

4. Complexity analysis

For both solutions, time complexity is O(n+m) where n and m are the lengths of the lists. Iterative space is O(1) extra space; recursive space is O(n+m) due to call stack. Mention that recursion depth could cause stack overflow for very long lists.

5. Discuss trade-offs and edge case handling

Compare iterative vs recursive: iterative is more space-efficient and avoids stack overflow, while recursive is more concise. Explain how duplicates are handled naturally by the comparison (using <= or < depending on stability). Confirm that empty lists are handled by returning the non-empty list.

Key Points to Mention

  • Use of a dummy node to simplify edge cases and avoid special handling for the head.
  • Time complexity O(n+m) for both approaches, where n and m are the lengths of the input lists.
  • Space complexity: O(1) for iterative, O(n+m) for recursive due to call stack.
  • Handling duplicates: preserve all nodes, typically by using <= in comparison to maintain stability.
  • Edge cases: empty lists, one list exhausted early, and lists of unequal length.
  • Trade-offs: iterative is preferred for production due to constant space; recursive is elegant but risks stack overflow.

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