Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Meta SWE interview with a nested list depth-weighted sum problem. They wanted both DFS and BFS implementations plus a follow-up on inverting the weights, which honestly tripped me up more than the core problem did.

Questions Asked (2)

Given a nested collection of integers and other nested collections, compute a depth-weighted sum where each integer is multiplied by its depth (1-based). Implement both a DFS and a BFS solution and analyze the time and space complexity of each.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

DFS felt natural, basically just recurse and pass the current depth down.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and defining the nested structure, then outline both DFS and BFS approaches. Implement each with clear code, and analyze time and space complexity, highlighting trade-offs.

Pro tip: Mention that BFS can be implemented iteratively with a queue to avoid recursion limits, but DFS is simpler and more memory-efficient for deep nesting. Also, note that both have O(N) time where N is total elements, but space differs: DFS O(D) for depth D, BFS O(W) for max width W.

1. Clarify and Define

Ask clarifying questions about the input format (e.g., list of integers and lists) and confirm depth definition (1-based). Define the problem and edge cases.

2. DFS Approach

Explain recursive DFS: traverse each element, if integer add value*depth, if list recurse with depth+1. Provide code and discuss recursion stack.

3. BFS Approach

Explain iterative BFS using a queue of (element, depth) pairs. Process level by level, summing integers multiplied by depth. Provide code.

4. Complexity Analysis

Analyze time: both O(N) where N is total number of integers and lists. Space: DFS O(D) for recursion depth, BFS O(W) for max width of queue.

5. Trade-offs and Conclusion

Compare DFS vs BFS: DFS simpler, less memory for deep structures; BFS avoids recursion limits, better for wide structures. Choose based on constraints.

Key Points to Mention

Time complexity O(N) for both, where N is total number of elements (integers and lists).
Space complexity: DFS O(D) for recursion stack, BFS O(W) for queue width.
DFS can be implemented recursively or iteratively with a stack.
BFS uses a queue and processes level by level, naturally handling depth.
Edge cases: empty list, deeply nested lists, non-integer elements (if any).
Trade-offs: recursion depth limits, memory usage, and clarity of code.

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

How would you adapt the solution if the weighting is inverted, meaning the deepest level gets weight 1 and shallower levels get higher weights?

Algorithms & Data StructuresTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This is where I stumbled.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the original problem and the current weighting scheme to ensure you understand the inversion. Then, explain how you would modify the algorithm or data structure to accommodate the new weights, focusing on the impact on traversal, accumulation, and complexity. Finally, discuss trade-offs and potential optimizations.

Pro tip: Demonstrate adaptability by relating this to a real-world scenario where weighting schemes change, and emphasize the importance of writing flexible code that can handle such variations with minimal changes.

1. Clarify the original problem and weighting

Restate the problem and the original weighting scheme to confirm understanding. Ask clarifying questions if needed, such as whether the tree is binary or n-ary, and how weights are applied.

2. Identify the impact of inversion

Analyze how inverting the weights affects the computation. For example, if originally deeper nodes had higher weights, now they have lower weights, which may change the traversal order or accumulation logic.

3. Propose modifications to the algorithm

Describe specific changes: e.g., if using DFS with depth tracking, adjust the weight calculation; if using BFS, consider how to incorporate depth-based weights. Mention any data structure changes.

4. Analyze complexity and trade-offs

Discuss time and space complexity changes, if any. Consider if the inversion allows for optimizations or requires additional passes.

5. Summarize and test

Summarize the adapted solution and outline how you would test it with examples, including edge cases like single node or skewed trees.

Key Points to Mention

Depth tracking during traversal (e.g., passing depth as parameter in recursion)
Weight calculation formula: original weight = depth, inverted weight = maxDepth - depth + 1 (or similar)
Impact on accumulation: sum of weighted values changes, may affect comparisons or thresholds
Potential need to precompute max depth or use two-pass approach
Time complexity remains O(n) for tree traversal, but constant factors may change
Space complexity considerations for recursive vs iterative approaches

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