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Early-stage Startup·Software Engineer·Technical Phone Screen·Junior

JuniorPending
Jun 2026Remote

Summary

Technical phone screen that went sideways on complexity. Got a working solution but it was n^4 and time ran out before I could clean it up, so now I'm just sitting here wondering if I'm cooked.

Questions Asked (1)

Q1

Solve a coding problem (roughly LeetCode easy/medium difficulty) and optimize your solution.

Algorithms & Data Structures
Author's notes

Got a working solution but the time complexity was n^4 which is pretty embarrassing in hindsight.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem and clarifying constraints and edge cases. Then walk through a brute-force solution, analyze its complexity, and iteratively optimize using appropriate data structures or algorithms. Finally, code the optimized solution, test it with examples, and discuss trade-offs.

Pro tip: At an early-stage startup, they value pragmatism and communication. Verbally explain your thought process and trade-offs, and show that you can balance code quality with speed of delivery.

1. Understand and Clarify

Restate the problem in your own words and ask clarifying questions about input size, constraints, and edge cases. Confirm expected output and any assumptions.

2. Brainstorm and Choose Approach

Discuss a brute-force solution first, then identify bottlenecks and propose optimizations. Choose the most efficient approach that fits the constraints, explaining why.

3. Code and Explain

Write clean, modular code while explaining each step. Use meaningful variable names and handle edge cases. Keep the code simple and readable.

4. Test and Validate

Walk through test cases, including edge cases, to verify correctness. If time permits, discuss potential bugs and how to fix them.

5. Analyze and Optimize Further

Analyze time and space complexity of your solution. Discuss any further optimizations or alternative approaches, and trade-offs between them.

Key Points to Mention

  • Time and space complexity analysis (Big O notation)
  • Edge cases and constraints (e.g., empty input, large input, duplicates)
  • Trade-offs between different approaches (e.g., time vs. space)
  • Use of appropriate data structures (e.g., hash maps, heaps, two pointers)
  • Code readability and maintainability
  • Testing methodology and validation

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