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Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediateRejected
Jun 2026Remote

Summary

Went through a Google coding round for a Software Engineer role and got rejected two days later. The feedback pointed to the coding session, which in hindsight made total sense once I replayed what actually happened in there.

Questions Asked (1)

Q1

Given a coding problem, walk through a brute-force solution before optimizing.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The interviewer literally suggested starting with brute force.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem and clarifying constraints, then present a straightforward brute-force solution with its time and space complexity. After establishing correctness, systematically identify inefficiencies and propose optimizations, explaining the trade-offs and validating the improved solution with examples.

Pro tip: Always quantify the brute-force complexity and explicitly connect each optimization to a specific bottleneck; this shows you understand the 'why' behind the improvement, not just the 'how'.

1. Clarify and Restate

Ask clarifying questions about input size, edge cases, and expected output. Restate the problem in your own words to confirm understanding.

2. Brute-Force Solution

Describe the simplest, most direct approach that solves the problem correctly, even if inefficient. Walk through a small example to demonstrate correctness.

3. Analyze Complexity

State the time and space complexity of the brute-force solution. Identify the specific operations or patterns that cause inefficiency.

4. Optimize Step-by-Step

Propose optimizations that target the bottlenecks, such as using a hash map, sorting, two pointers, or dynamic programming. Explain how each change improves complexity.

5. Validate and Compare

Test the optimized solution on edge cases and compare its complexity to the brute-force. Discuss trade-offs like readability vs. performance.

Key Points to Mention

  • Time and space complexity of both brute-force and optimized solutions
  • Specific data structures or algorithms used for optimization (e.g., hash map, two pointers, binary search)
  • Trade-offs between simplicity, readability, and performance
  • Edge cases and how they are handled in both solutions
  • Correctness proof or reasoning for the optimized approach
  • Potential further optimizations or alternative approaches

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