Bytedance·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Bytedance ML engineer interview with a classic dynamic programming problem. Nothing too exotic but the pressure of getting it right on the spot is real.

Questions Asked (1)

You have a row of houses, each with some amount of money. You can't rob two houses next to each other. What's the maximum you can rob?

Algorithms & Data Structures

Author's notes

Classic DP, I knew it the second they described it.

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AI HintsAI Generated

Suggested Approach

Recognize this as the classic House Robber dynamic programming problem. Explain that you can solve it in O(n) time and O(1) space by keeping track of the maximum loot up to the previous two houses. Walk through the recurrence relation and provide a clear example to illustrate.

Pro tip: Mention that this problem tests your ability to identify optimal substructure and overlapping subproblems, which are core to DP and also relevant to ML (e.g., sequence modeling). Also, discuss edge cases like empty input or single house to show thoroughness.

1. Clarify the problem

Confirm that houses are in a line, each has a non-negative amount, and you cannot rob adjacent houses. Ask if the input can be empty or contain one house.

2. Define the DP state

Let dp[i] be the maximum amount you can rob from the first i houses. The recurrence is dp[i] = max(dp[i-1], dp[i-2] + nums[i]).

3. Optimize space

Since dp[i] only depends on the previous two values, use two variables to achieve O(1) space instead of an array.

4. Walk through an example

Use a small example like [2,7,9,3,1] to demonstrate how the recurrence works and verify the result (12).

5. Analyze complexity and edge cases

State time complexity O(n) and space O(1). Handle edge cases: empty array returns 0, single house returns its value.

Key Points to Mention

Dynamic programming approach with optimal substructure
Recurrence relation: dp[i] = max(dp[i-1], dp[i-2] + nums[i])
Space optimization using two variables
Time complexity O(n) and space complexity O(1)
Edge cases: empty input, single house, all houses same value
Connection to ML: sequence modeling and DP in reinforcement learning

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