Netflix·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Netflix coding interview where they had me solve a tree depth problem and write my own test cases on the spot. Not the hardest problem but the test case part tripped me up more than I expected.

Questions Asked (1)

Implement a solution to compute the maximum depth of a binary tree, then write your own test cases covering edge cases like an empty tree, a single node, a skewed tree, and a balanced tree.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The actual algorithm wasn't the hard part, I had that in a few minutes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then present a clean recursive solution with O(n) time and O(h) space, and finally walk through comprehensive test cases including edge cases. Emphasize the trade-offs between recursive and iterative approaches and how you would validate correctness.

Pro tip: Mention that you would also test with a large tree to ensure no stack overflow in recursion, and discuss converting to an iterative BFS/DFS if needed for production robustness.

1. Clarify requirements and constraints

Ask about input format, tree node definition, and any constraints like maximum depth or memory limits. Confirm whether the tree is binary and if null nodes are represented explicitly.

2. Design the algorithm

Propose a recursive depth-first search: max depth = 1 + max(depth(left), depth(right)). Discuss time and space complexity and alternative iterative approaches.

3. Implement the solution

Write clean code with proper base case (null node returns 0) and recursive calls. Handle edge cases like empty tree and single node naturally.

4. Develop test cases

Create tests for empty tree, single node, skewed tree (left/right), balanced tree, and a larger tree. Include expected outputs and consider using a testing framework.

5. Analyze and discuss trade-offs

Compare recursive vs iterative solutions in terms of readability, stack usage, and performance. Mention potential optimizations or variations.

Key Points to Mention

Recursive DFS solution with O(n) time and O(h) space complexity
Base case: null node returns 0
Edge cases: empty tree (depth 0), single node (depth 1), skewed tree (depth n), balanced tree (depth log n)
Iterative alternative using BFS level-order traversal or stack-based DFS
Testing strategy: unit tests with assertions, possibly using a framework like JUnit or pytest
Trade-offs: recursion simplicity vs stack overflow risk for deep trees; iterative avoids recursion limit but may be more complex

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