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Early-stage Startup·Software Engineer·Technical Phone Screen·Junior

JuniorRejected
Jun 2026

Summary

Went into a junior coding interview assuming DP wouldn't come up, got burned by exactly that assumption.

Questions Asked (1)

Q1

Solve an introductory dynamic programming problem.

Algorithms & Data Structures
Author's notes

Completely froze.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then define the state and recurrence relation before coding. Implement a bottom-up DP solution, optimize space if possible, and test with edge cases.

Pro tip: Always discuss time and space complexity trade-offs, and mention how you would optimize for the startup's scale. Showing awareness of practical constraints like memory usage can set you apart.

1. Clarify the problem

Ask questions to confirm input/output format, constraints, and edge cases. Ensure you understand what the DP is optimizing.

2. Define the DP state

Clearly state what each DP cell represents (e.g., dp[i] = max value up to index i). This is crucial for a correct recurrence.

3. Derive recurrence relation

Express dp[i] in terms of previous states. Explain the transition logic and base cases.

4. Implement and optimize

Code the solution, starting with a straightforward bottom-up approach. Then consider space optimization (e.g., using rolling variables).

5. Test and analyze

Walk through examples, including edge cases. State time and space complexity, and discuss potential improvements.

Key Points to Mention

  • Optimal substructure and overlapping subproblems
  • Time and space complexity analysis (Big O notation)
  • Space optimization techniques (e.g., 1D array instead of 2D)
  • Edge cases (empty input, single element, large values)
  • Comparison with other approaches (e.g., greedy, recursion with memoization)
  • Code clarity and modularity for maintainability

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