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

JuniorPending
Jul 2026Remote

Summary

Technical coding round where I got through a medium/easy problem but leaned on the interviewer's hints more than I'd like to admit, and the solution I landed on was way too slow. Ran out of time before I could optimize it, so now I'm just waiting to see if communicating well was enough to save me.

Questions Asked (1)

Q1

Solve a coding algorithm problem (described as medium or easy difficulty on LeetCode).

Algorithms & Data Structures
Author's notes

Got to a working solution but the time complexity was n^4 which is pretty rough.

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AI HintsAI Generated

Suggested Approach

Start by restating the problem in your own words and asking clarifying questions to confirm assumptions about input size, edge cases, and constraints. Then walk through a brute-force solution, analyze its time and space complexity, and iteratively optimize using appropriate data structures or algorithmic patterns. Finally, code the optimized solution cleanly, test it with examples and edge cases, and discuss trade-offs.

Pro tip: At an early-stage startup, they value engineers who can ship working code quickly and iterate. So, after presenting the optimal solution, mention a simpler approach that could be implemented faster if time-to-market is critical, showing you understand business trade-offs.

1. Understand and Clarify

Restate the problem, ask about input constraints, expected output, and edge cases. Confirm assumptions before proceeding.

2. Explore Examples

Walk through a few test cases, including edge cases, to ensure you understand the problem and to identify patterns.

3. Brainstorm Approaches

Start with a brute-force solution, then discuss potential optimizations using data structures or algorithmic techniques. Analyze time and space complexity for each.

4. Implement and Test

Write clean, modular code for the chosen approach. Test with the examples and edge cases, debugging as needed.

5. Review and Optimize

Review code for correctness and efficiency. Discuss possible further optimizations or trade-offs, and consider follow-up questions.

Key Points to Mention

  • Time and space complexity analysis for each approach
  • Edge cases such as empty input, single element, large input, duplicates, negative numbers
  • Choice of data structures (e.g., hash map, heap, stack) and why they are appropriate
  • Trade-offs between different solutions (e.g., simplicity vs. efficiency)
  • Code readability and maintainability (naming, modularity, comments)
  • Testing strategy including unit tests and handling of invalid inputs

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