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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePending
Jun 2026Remote

Summary

Coding interview at Google for a Software Engineer role. The question itself wasn't brutal but the execution was shaky, lots of fumbling mid-explanation before things came together. Still has a behavioral round left and is genuinely unsure if this was enough to move forward.

Questions Asked (1)

Q1

Given an algorithm problem, optimize from a brute force solution to an optimal one, then analyze time and space complexity, and finally reduce space usage to O(1).

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Panicked mid-explanation trying to derive the optimal approach on the fly and just started fumbling the math out loud.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating the brute force solution and its complexity, then systematically identify inefficiencies and propose optimizations, explaining the trade-offs. After reaching an optimal time complexity, analyze space usage and explore techniques to reduce it to O(1) without sacrificing time efficiency.

Pro tip: Always verbalize your thought process and consider edge cases; interviewers value clear reasoning and the ability to iterate on solutions more than immediately finding the perfect answer.

1. Understand and Clarify the Problem

Restate the problem in your own words, ask clarifying questions about input constraints, expected output, and edge cases to ensure you fully understand the requirements.

2. Present Brute Force Solution

Describe a straightforward, naive approach to solve the problem, and analyze its time and space complexity to establish a baseline.

3. Optimize Time Complexity

Identify bottlenecks in the brute force solution and propose improvements using appropriate data structures or algorithmic techniques (e.g., two pointers, hashing, sorting) to reduce time complexity.

4. Analyze and Reduce Space Complexity

After achieving optimal time, examine space usage and explore methods to reduce it, such as in-place modifications, bit manipulation, or reusing input, aiming for O(1) auxiliary space.

5. Test and Validate

Walk through the optimized solution with sample inputs, including edge cases, to verify correctness and ensure the complexity claims hold.

Key Points to Mention

  • Time-space trade-offs: sometimes improving time increases space, and vice versa; discuss how you balance them.
  • Common optimization techniques: two pointers, sliding window, hashing, sorting, dynamic programming, greedy algorithms.
  • Space reduction strategies: in-place algorithms, bit manipulation, using variables instead of arrays, and reusing input.
  • Complexity analysis: clearly state Big-O for time and space, and explain how you derived them.
  • Edge cases: empty input, single element, large input, duplicates, negative numbers, etc.
  • Communication: explain your reasoning step-by-step and welcome hints from the interviewer.

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