LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

LinkedIn SWE interview with a recursive array problem. Pretty standard algorithmic question but the complexity discussion at the end is where things got interesting.

Questions Asked (1)

Given a nested array of arbitrary depth where elements can be numbers or further nested arrays, write a function that sums all numeric values across all levels. For example, sum([[1, 2], [3, [4, 5]], 6]) should return 21. You can use recursion or an explicit stack. Also discuss time and space complexity in terms of total element count and maximum nesting depth.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The coding part was fine, recursion felt natural here and I got it working pretty quickly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present both recursive and iterative solutions, highlighting trade-offs. Finally, analyze time and space complexity in terms of total elements (N) and maximum depth (D), and discuss potential optimizations like tail recursion or iterative deepening.

Pro tip: Mention that recursion depth could cause stack overflow for very deep arrays, so an explicit stack is safer in production; also note that JavaScript's Array.flat(Infinity) is not allowed here but shows awareness of built-ins.

1. Clarify requirements and edge cases

Ask about input constraints: can arrays be empty? Can numbers be negative or floating-point? What about non-numeric values? Confirm that the function should handle arbitrary depth and return 0 for empty arrays.

2. Present recursive solution

Write a recursive function that iterates through the array, adding numbers and recursively calling itself on nested arrays. Explain base case and recursive step clearly.

3. Present iterative solution with explicit stack

Implement an iterative version using a stack (or queue) to avoid recursion depth limits. Push all elements onto the stack, pop and process each, pushing nested arrays back onto the stack.

4. Analyze time and space complexity

Time complexity is O(N) where N is total number of elements (including nested arrays). Space complexity: recursion uses O(D) call stack, iterative uses O(N) worst-case stack; discuss trade-offs.

5. Discuss trade-offs and optimizations

Compare recursion vs iteration: recursion is cleaner but risks stack overflow; iteration is more robust but uses extra space. Mention tail recursion optimization (if language supports) or using a generator to lazily flatten.

Key Points to Mention

Time complexity O(N) where N is total elements (numbers + arrays).
Space complexity: recursion O(D) where D is max depth; iterative O(N) worst-case.
Recursion may cause stack overflow for deep nesting; iterative avoids this.
Edge cases: empty array, non-numeric values, negative numbers, floating-point precision.
Trade-off between code simplicity (recursion) and robustness (iteration).
Potential optimization: tail recursion if language supports, or using a stack with manual loop.

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