Bytedance·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Bytedance ML engineer interview that came down to a classic n-queens backtracking problem. I tried to get clever with an iterative stack-based approach instead of the standard recursion and paid for it.

Questions Asked (1)

Implement a solution to the n-queens problem that returns all distinct board configurations, where no two queens share a row, column, or diagonal.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went iterative with an explicit stack instead of just doing the clean recursive backtracking everyone knows.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., board size, output format) and then present a backtracking solution that places queens row by row, using sets to track occupied columns and diagonals. Emphasize the time complexity O(N!) and space complexity O(N^2) for storing results, and discuss potential optimizations like bitmasking for speed.

Pro tip: Mention that while the problem is classic, you can demonstrate ML engineering maturity by relating it to constraint satisfaction problems in ML (e.g., hyperparameter tuning) and by discussing how to parallelize or prune the search space for large N.

1. Clarify requirements and constraints

Ask about input size, expected output format, and whether all solutions are needed or just count. Confirm if N is small enough for backtracking or if optimizations are required.

2. Outline backtracking approach

Explain that you'll place queens row by row, maintaining sets for columns and diagonals to check validity in O(1) time. Describe the recursive function that tries all columns for the current row and backtracks when no valid placement exists.

3. Detail implementation and data structures

Specify using a list to represent the board (e.g., queen positions per row), and sets for columns, diagonals (row+col), and anti-diagonals (row-col). Mention that when a solution is found, convert the board to the required output format (e.g., list of strings).

4. Analyze complexity and trade-offs

State that time complexity is O(N!) in the worst case, but pruning reduces it significantly. Space complexity is O(N) for recursion and sets, plus O(N^2) per solution stored. Discuss trade-offs between storing all solutions vs. streaming them.

5. Discuss optimizations and extensions

Mention bitmasking to represent occupied columns and diagonals for faster checks, or using symmetry reduction to avoid duplicate solutions. Relate to ML: constraint satisfaction, search algorithms, and potential parallelization.

Key Points to Mention

Backtracking with pruning: place queens row by row, only try valid columns.
Use of sets for O(1) validity checks: columns, diagonals (row+col), anti-diagonals (row-col).
Time complexity O(N!) and space complexity O(N^2) for storing all solutions.
Bitmasking optimization: represent occupied columns and diagonals as integers for faster checks.
Symmetry reduction: avoid mirrored solutions to cut search space in half.
Connection to ML: constraint satisfaction, hyperparameter search, and parallelization.

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