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Samsung·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Got a coding question at Samsung for an ML Engineer role, classic dynamic programming stuff. Nothing too wild but the space optimization part is where they seem to actually care.

Questions Asked (1)

Q1

Given an array of non-negative integers representing money in each house on a street, find the maximum amount you can steal without robbing two adjacent houses.

Algorithms & Data Structures
Author's notes

I knew this problem but still fumbled explaining the recurrence out loud.

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AI HintsAI Generated

Suggested Approach

Recognize this as the classic House Robber problem and immediately frame it as a dynamic programming problem. Define the state as the maximum amount robbed up to house i, then derive the recurrence dp[i] = max(dp[i-1], dp[i-2] + nums[i]). Optimize space to O(1) by keeping only the last two values.

Pro tip: Mention that this problem is equivalent to finding a maximum weight independent set on a path graph, which shows deeper algorithmic insight. Also, explicitly discuss edge cases like empty array, single house, and all zeros to demonstrate thoroughness.

1. Clarify and Confirm

Restate the problem to ensure understanding: non-negative integers, cannot rob adjacent houses, maximize sum. Ask about constraints (e.g., array size, values) if not given.

2. Define DP State and Recurrence

Let dp[i] be the max amount from first i houses. Recurrence: dp[i] = max(dp[i-1], dp[i-2] + nums[i]). Base cases: dp[0]=0, dp[1]=nums[0].

3. Optimize Space

Observe that only dp[i-1] and dp[i-2] are needed. Use two variables to achieve O(1) space while maintaining O(n) time.

4. Handle Edge Cases

Check for empty array (return 0), single house (return its value), and all zeros (return 0). Ensure code handles these gracefully.

5. Analyze Complexity and Test

State time O(n) and space O(1). Walk through a small example (e.g., [2,7,9,3,1]) to verify recurrence and edge cases.

Key Points to Mention

  • Dynamic programming state definition and recurrence relation
  • Space optimization from O(n) to O(1) using two variables
  • Time and space complexity analysis (O(n) time, O(1) space)
  • Edge cases: empty array, single house, all zeros
  • Connection to maximum weight independent set on a path graph
  • Comparison with brute-force and greedy approaches (why greedy fails)

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