Meta·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate

Apr 2026

Summary

Meta's OA for SWE roles isn't what you'd expect from a typical coding screen. Instead of a single algorithm problem, you get a feature implementation task broken into four progressive levels, where each level builds on the last and everything has to keep working.

Questions Asked (1)

Implement a feature across four progressive levels, where each level extends the previous one with additional capabilities, constraints, or complexity, and all prior levels must continue to pass.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

The structure tripped me up more than the actual coding.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints for each level, then design a modular solution that can be extended incrementally. Implement and test each level before moving to the next, ensuring backward compatibility. Discuss trade-offs and potential optimizations as you progress.

Pro tip: Emphasize testability and maintainability from the start; write unit tests for each level to catch regressions early. Also, proactively discuss how you would scale the solution for Meta's large-scale systems.

1. Clarify Requirements

Ask questions to understand the feature's purpose, expected inputs/outputs, and constraints for each level. Confirm assumptions with the interviewer.

2. Design Extensible Architecture

Sketch a high-level design that separates core logic from level-specific extensions. Use interfaces or abstract classes to allow easy addition of new capabilities.

3. Implement Incrementally

Code level 1 first, then extend to level 2, ensuring previous functionality remains intact. Write tests for each level to verify correctness and backward compatibility.

4. Analyze Trade-offs

For each level, discuss time/space complexity, potential bottlenecks, and alternative approaches. Consider how the solution would scale with increased data or users.

5. Review and Optimize

Refactor code for clarity and performance, and suggest possible optimizations for future levels. Summarize how the solution meets all requirements.

Key Points to Mention

Modular design principles (e.g., separation of concerns, open/closed principle)
Incremental development and testing (unit tests, regression tests)
Time and space complexity analysis for each level
Trade-offs between different data structures or algorithms
Scalability and performance considerations for large-scale systems
Backward compatibility and extensibility

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