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Bytedance·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say
May 2026

Summary

ByteDance SRE onsite with a LeetCode problem at the very end. Tight on time but managed to pass all 15 test cases on the first run, which felt pretty good.

Questions Asked (1)

Q1

Solve a coding algorithm problem under time pressure, with test cases run live immediately after you finish writing.

Algorithms & Data Structures
Author's notes

Only had about 10 minutes left when they dropped the coding problem on me.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases first, then outline your algorithm verbally before coding. Write clean, modular code with meaningful variable names, and test with provided examples plus your own edge cases. If time permits, discuss time/space complexity and potential optimizations.

Pro tip: Narrate your thought process continuously—interviewers evaluate how you think, not just the final code. If you get stuck, simplify the problem or start with a brute-force solution and iterate.

1. Understand and Clarify

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

2. Plan the Approach

Verbally outline your algorithm, including data structures and steps. Discuss trade-offs between multiple approaches if applicable, and choose the most efficient one given constraints.

3. Code with Clarity

Write clean, well-structured code with meaningful names and comments. Modularize if helpful, and avoid overly clever one-liners that are hard to debug.

4. Test and Debug

Run through provided examples manually, then test edge cases (empty input, large values, duplicates). If tests fail, debug systematically by isolating the issue.

5. Analyze and Optimize

State the time and space complexity of your solution. If there's time, suggest potential optimizations or alternative approaches.

Key Points to Mention

  • Time and space complexity analysis (Big O notation)
  • Edge cases: empty input, single element, duplicates, negative numbers, overflow
  • Choice of data structures (e.g., hash map, heap, two pointers) and why
  • Trade-offs between different approaches (e.g., brute force vs. optimized)
  • Code readability and maintainability (naming, modularity, comments)
  • Testing strategy: unit tests, boundary cases, and stress testing

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