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Series B+ Startup·Software Engineer·Onsite - Multi Round·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Six-hour onsite for a software engineer role, and the technical portion ended up being the most frustrating part. The hiring manager had specifically told me it wouldn't be a leetcode-style question, so I stopped my daily practice a week out to prep for git and repository workflows instead. Showed up and it was a leetcode medium sitting in a github repo for no reason other than hosting.

Questions Asked (1)

Q1

Solve a medium-difficulty algorithmic coding problem provided via a GitHub repository, using an IDE of your choice.

Algorithms & Data StructuresAdaptability & Ambiguity
Author's notes

The cruel part is I'd been doing this stuff every day for months and stopped a week before the interview because I was told it wouldn't be this.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem requirements and constraints by restating them and asking questions. Then, outline a brute-force solution and optimize it using appropriate data structures and algorithms, explaining your reasoning as you code. Finally, test with edge cases and discuss time/space complexity.

Pro tip: While coding, verbalize your thought process and trade-offs to demonstrate adaptability and problem-solving skills. If you get stuck, take a step back and consider simpler approaches or analogies.

1. Understand the problem

Restate the problem in your own words, ask clarifying questions about input/output, constraints, and edge cases. Confirm your understanding with the interviewer.

2. Plan the approach

Discuss a brute-force solution first, then propose an optimized approach. Explain the data structures and algorithms you'll use and why.

3. Implement the solution

Write clean, modular code in your IDE, explaining each step. Use meaningful variable names and handle edge cases as you go.

4. Test and debug

Run through test cases, including edge cases, and debug any issues. Verbally walk through the code's execution.

5. Analyze complexity

State the time and space complexity of your solution and discuss potential improvements or trade-offs.

Key Points to Mention

  • Clarifying questions to resolve ambiguity
  • Brute-force vs optimized solution trade-offs
  • Choice of data structures and algorithms
  • Time and space complexity analysis
  • Edge cases and testing strategy
  • Code readability and maintainability

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