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Google·Software Engineer·Onsite - Multi Round·Intermediate

IntermediateRejected
May 2025

Summary

Interviewed at Google for a software engineering role in May. Behavioral went smoothly and the technical round felt solid too, but still got a generic rejection email with zero feedback after the weekend. No idea what actually sank it.

Questions Asked (2)

Q1

Walk through a coding problem, including edge cases, time complexity, and a follow-up extension.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Solved the main problem without any hints, covered edge cases, walked through examples, coded it up cleanly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a brute-force solution before optimizing. Walk through your optimized approach with a concrete example, explicitly analyzing time and space complexity, and finally discuss edge cases and a follow-up extension.

Pro tip: At Google, interviewers value structured communication and the ability to iterate on feedback. After presenting your solution, proactively suggest a follow-up extension (e.g., handling larger inputs or adding a feature) to demonstrate depth and initiative.

1. Clarify the Problem

Ask questions to understand input/output, constraints, and assumptions. Confirm edge cases like empty input, large values, or duplicates.

2. Discuss Brute Force

Outline a simple, naive solution and its complexity. This shows you can start somewhere and provides a baseline for optimization.

3. Optimize and Explain

Propose an improved algorithm, explaining your thought process. Use a small example to illustrate how it works.

4. Analyze Complexity

Clearly state the time and space complexity of your optimized solution, and compare it to the brute force.

5. Handle Edge Cases and Extend

Walk through edge cases and how your solution handles them. Then suggest a follow-up extension or variation to show depth.

Key Points to Mention

  • Time and space complexity analysis (Big O notation)
  • Edge cases: empty input, single element, duplicates, large inputs, negative numbers
  • Trade-offs between different approaches (e.g., time vs. space)
  • Code readability and modularity (if writing code)
  • Testing strategy: unit tests, boundary cases
  • Follow-up extension: scaling, additional constraints, or feature enhancement

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

Q2

Behavioral questions about past experiences and how you handled specific situations.

Adaptability & AmbiguityConflict Resolution
Author's notes

This part went well.

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AI HintsAI Generated

Suggested Approach

Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on specific examples that highlight adaptability and conflict resolution. Emphasize your thought process, the actions you took, and the measurable outcomes, while showing how you navigated ambiguity or resolved disagreements.

Pro tip: Choose examples where you made a tangible impact, and quantify results when possible. Also, briefly reflect on what you learned, demonstrating self-awareness and growth.

1. Set the Context

Briefly describe the situation and the task you were responsible for, providing enough background for the interviewer to understand the challenge.

2. Highlight the Challenge

Explain the specific ambiguity or conflict you faced, and why it was significant or difficult.

3. Detail Your Actions

Walk through the steps you took to address the situation, emphasizing your reasoning, collaboration, and problem-solving skills.

4. Share the Outcome

Describe the results of your actions, including any measurable impact, and what you learned from the experience.

Key Points to Mention

  • Specific examples of adapting to changing requirements or unclear instructions
  • Techniques for resolving disagreements, such as active listening and finding common ground
  • Your thought process when making decisions under uncertainty
  • Collaboration with cross-functional teams or stakeholders
  • Quantifiable results or positive feedback from the outcome
  • Lessons learned and how you applied them in future situations

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