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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediateRejected
Jun 2026

Summary

Google SWE coding interview that went sideways fast. Got hit with a 2D DP problem that apparently requires knowing some niche algorithm, and even after grinding through hundreds of LC problems, I wasn't ready for it.

Questions Asked (1)

Q1

Solve a 2D dynamic programming problem that requires knowledge of a specialized algorithm.

Algorithms & Data Structures
Author's notes

Described the brute force approach verbally with complexity analysis but didn't code it up, then tried jumping straight to bottom-up DP and completely fell apart.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and identify the state variables and recurrence relation. Then, recognize the specialized algorithm (e.g., Knuth's optimization, divide and conquer DP, or convex hull trick) that applies, and implement it efficiently while explaining the time and space complexity.

Pro tip: Verbalize your thought process: start with a brute-force DP, then show how the specialized algorithm optimizes it, demonstrating deep understanding and structured problem-solving.

1. Clarify the problem

Ask questions to confirm input size, constraints, and expected output. Restate the problem in your own words to ensure alignment.

2. Define DP state and recurrence

Identify the state variables and write the recurrence relation. Discuss base cases and how the answer is derived.

3. Identify the specialized algorithm

Recognize patterns (e.g., monotonicity, quadrangle inequality) that allow optimization. Name the algorithm and explain why it applies.

4. Implement and optimize

Write pseudocode for the optimized DP, ensuring correct order of computation. Analyze time and space complexity.

5. Test and discuss trade-offs

Walk through a small example to verify correctness. Mention alternative approaches and trade-offs.

Key Points to Mention

  • State definition and recurrence relation
  • Time and space complexity of brute-force vs optimized solution
  • Conditions for applying the specialized algorithm (e.g., quadrangle inequality, monotonicity)
  • Implementation details (e.g., loop order, data structures)
  • Edge cases and handling of large inputs
  • Potential pitfalls and how to avoid them

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