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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Sep 2026

Summary

Google SWE interview that went sideways pretty fast. Got stuck on a math constraint for half the session and never actually wrote any code, which was the whole point.

Questions Asked (1)

Q1

Given a coding problem, derive and prove the underlying mathematical constraint that makes an optimal solution possible, then implement it.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Tried brute force first and got redirected.

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AI HintsAI Generated

Suggested Approach

First, restate the problem and identify the key variables and constraints. Then, derive the mathematical condition (e.g., invariant, inequality, or monotonicity) that characterizes an optimal solution, and prove it rigorously. Finally, implement the algorithm based on that condition, ensuring efficiency.

Pro tip: Verbalize your thought process clearly and connect the math to the code; interviewers value structured reasoning over a rushed solution. If stuck, simplify the problem to a smaller case to uncover the pattern.

1. Understand and Restate

Clarify the problem, inputs, outputs, and constraints. Restate it in your own words to ensure alignment.

2. Explore and Hypothesize

Examine small examples to identify patterns or invariants. Form a hypothesis about the mathematical constraint that enables optimality.

3. Derive and Prove

Formally derive the constraint (e.g., using induction, exchange argument, or contradiction) and prove it guarantees an optimal solution.

4. Design Algorithm

Translate the proven constraint into an algorithm, choosing appropriate data structures and analyzing time/space complexity.

5. Implement and Test

Write clean code, then test with edge cases and validate against the derived constraint.

Key Points to Mention

  • Time and space complexity analysis of the proposed solution
  • Proof techniques such as induction, exchange argument, or contradiction
  • Edge cases and how the constraint handles them
  • Trade-offs between different approaches (e.g., greedy vs. dynamic programming)
  • Connection between the mathematical constraint and the algorithm's correctness
  • Potential optimizations or alternative implementations

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