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Meta·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Coding round at Meta for a Software Engineer role, two algorithmic problems back to back. Nothing behavioral, just straight into the problems. Tree stuff and matrix traversal, which is pretty standard Meta territory from what I've heard.

Questions Asked (2)

Q1

Given an array of distinct integers representing the preorder traversal of a BST, reconstruct the original tree and return its root. A linear time solution is expected.

Algorithms & Data Structures
Author's notes

I knew preorder means root comes first, so that part clicked fast.

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AI HintsAI Generated

Suggested Approach

Use a stack to simulate the recursive construction of the BST from the preorder traversal. Iterate through the array, maintaining a stack of nodes and a lower bound for each node to decide whether to attach as left or right child. This yields an O(n) time and O(n) space solution.

Pro tip: Clarify that the input is guaranteed to be a valid preorder traversal of a BST with distinct integers, so no validation is needed. Also mention that the stack-based approach is essentially an iterative version of the recursive method with a lower bound, and it avoids recursion depth issues.

1. Understand the problem and constraints

Confirm that the array is a preorder traversal of a BST with distinct integers, and that we need to reconstruct the tree in linear time. Discuss the properties of preorder traversal: root first, then left subtree, then right subtree.

2. Choose an approach

Decide between recursive and iterative methods. The recursive method uses a lower bound and index pointer, while the iterative method uses a stack. Both are O(n) time, but the iterative one is often preferred to avoid recursion depth issues.

3. Implement the stack-based algorithm

Initialize an empty stack and set the root as the first element. Iterate through the remaining elements: while the current value is greater than the stack top, pop and update the last popped node as the parent for the next insertion. If the current value is less than the stack top, attach as left child; otherwise, attach as right child of the last popped node. Push the new node onto the stack.

4. Analyze complexity and edge cases

Explain that each node is pushed and popped at most once, so time is O(n) and space is O(n) for the stack. Handle edge cases: empty array returns null, single element returns a leaf node.

5. Test with examples

Walk through a small example, e.g., [8,5,1,7,10,12], to demonstrate how the stack and pointers update. Verify the reconstructed tree matches the expected BST.

Key Points to Mention

  • Preorder traversal properties: root, left subtree, right subtree.
  • BST property: left child < parent < right child.
  • Stack-based iterative construction with a lower bound.
  • Time complexity O(n) and space complexity O(n).
  • Handling edge cases: empty array, single node.
  • Comparison with recursive approach and why iterative is preferred.

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

Q2

Given an m x n integer matrix, return all elements visited in clockwise spiral order, peeling off the outer boundary layer by layer until the whole matrix is covered.

Algorithms & Data Structures
Author's notes

Classic spiral matrix.

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AI HintsAI Generated

Suggested Approach

Use a layer-by-layer simulation with four boundaries (top, bottom, left, right) to traverse the matrix in clockwise order. After traversing each side, shrink the corresponding boundary and repeat until all elements are visited. This approach is intuitive, easy to implement, and runs in O(m*n) time with O(1) extra space (excluding output).

Pro tip: Clarify edge cases upfront (empty matrix, single row/column) and mention that you'll handle them explicitly to avoid index errors. Also, discuss the trade-offs: while simulation is straightforward, a recursive peeling approach can be more elegant but may risk stack overflow for large matrices.

1. Understand the problem and edge cases

Restate the problem to ensure clarity, and list edge cases such as empty matrix, single row, single column, and non-square matrices. This shows thoroughness and prevents bugs.

2. Define boundaries and traversal order

Initialize four boundaries: top=0, bottom=m-1, left=0, right=n-1. Explain that you'll traverse right along top, down along right, left along bottom, and up along left, in that order.

3. Simulate layer-by-layer traversal

Use a while loop (top <= bottom and left <= right) to process each layer. After each direction, update the corresponding boundary and check for termination conditions to avoid duplicates.

4. Handle remaining elements in the last layer

When boundaries converge, ensure that single row or column cases are handled correctly by adding conditional checks before traversing left or up.

5. Analyze complexity and test

State that time complexity is O(m*n) since each element is visited once, and space complexity is O(1) extra (excluding output). Walk through a small example to verify correctness.

Key Points to Mention

  • Boundary variables (top, bottom, left, right) and how they shrink after each traversal.
  • Order of traversal: left-to-right, top-to-bottom, right-to-left, bottom-to-top.
  • Termination condition: while top <= bottom and left <= right.
  • Edge cases: empty matrix, single row, single column, and matrices with odd dimensions.
  • Time and space complexity: O(m*n) time, O(1) extra space.
  • Avoiding duplicate visits by checking boundaries before traversing left or up.

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