← Bytedance Interview Insights

Bytedance·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026

Summary

Bytedance ML engineer interview, got a tree reconstruction problem that I've seen before but still managed to second-guess myself on the implementation details.

Questions Asked (1)

Q1

Given the preorder and inorder traversal arrays of a binary tree, reconstruct the original tree and return its root.

Algorithms & Data Structures
Author's notes

I knew the trick going in: preorder gives you the root, inorder tells you where to split left and right subtrees.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Use the fact that the first element of preorder is the root, then find its index in inorder to split left and right subtrees. Recursively build the tree, using a hash map to achieve O(1) index lookups and O(n) overall time.

Pro tip: Mention that you can avoid slicing arrays by passing index ranges, which reduces space complexity and shows attention to performance. Also, discuss handling edge cases like empty arrays or duplicate values (if allowed) to demonstrate thoroughness.

1. Identify the root

The first element of the preorder array is the root of the current subtree.

2. Locate root in inorder

Find the index of the root value in the inorder array; this splits the inorder into left and right subtrees.

3. Determine subtree sizes

Calculate the number of nodes in the left subtree (root index in inorder) to know how many elements belong to the left in preorder.

4. Recursively build subtrees

Recursively construct the left and right subtrees using the corresponding subarrays of preorder and inorder.

5. Optimize with hash map

Precompute a hash map from value to index in inorder to achieve O(1) lookups, reducing time complexity to O(n).

Key Points to Mention

  • Time and space complexity: O(n) time with hash map, O(n) space for recursion and map.
  • Handling edge cases: empty arrays, single node, skewed trees.
  • Avoiding array slicing by using index ranges to save memory.
  • Assumption of unique values; if duplicates exist, need additional handling.
  • Recursive vs iterative approaches (e.g., using stack).
  • Application in ML: tree-based models like decision trees, random forests.

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