Coming in as an MLE candidate I half-expected something model-related, so the pure DSA format was a bit of a reset.
Start by clarifying the problem constraints and edge cases, then discuss the brute-force approach and its complexity. Optimize using appropriate data structures or algorithms (e.g., hashmap, two pointers, BFS/DFS, DP), and walk through your solution with a small example before coding. Finally, analyze time and space complexity and test with edge cases.
Pro tip: For ML Engineer roles at Meta, emphasize how you think about scalability and trade-offs, and relate the problem to real-world ML scenarios if possible. Also, communicate clearly and check in with the interviewer frequently.
Ask clarifying questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then identify bottlenecks and propose optimized approaches using suitable data structures or algorithms.
Choose a small but non-trivial example to dry-run your chosen approach, verifying correctness and handling edge cases.
Write clean, modular code with meaningful variable names. Test with the example and additional edge cases, debugging as needed.
State the time and space complexity of your solution, and discuss potential improvements or trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.