Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Meta data engineer round, one coding-style question about dictionary manipulation and capacity redistribution. Pretty algorithmic for a DE role but not shocking given Meta's bar. Short session, nothing behavioral.

Questions Asked (1)

You're given a nested dictionary where each entry maps a closed site to a destination site and the additional capacity needed there. Each site also has a baseline capacity stored at the self-referential key. Given a set of closed sites, compute the final capacity for each remaining open site by summing its baseline with any redistribution from closed sites. Return only the open sites in the output.

Algorithms & Data StructuresData Modeling

Author's notes

The self-referential baseline thing tripped me up for a second.

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AI HintsAI Generated

Suggested Approach

Clarify the data structure and requirements, then propose an algorithm that iterates through closed sites and accumulates their redistributed capacity into destination sites. Use a hash map to track final capacities, initializing with baselines for open sites and updating as you process each closed site. Finally, filter out closed sites and return the open sites with their computed capacities.

Pro tip: Mention edge cases like cycles or chains of redistribution (e.g., a closed site pointing to another closed site) and how your approach handles them, showing thoroughness. Also, discuss time and space complexity to demonstrate efficiency awareness.

1. Clarify the problem

Ask questions to confirm the data structure: each site has a baseline capacity at a self-referential key, and closed sites map to destination sites with additional capacity. Confirm that redistribution is only from closed sites to open sites, and that closed sites are removed from the output.

2. Design the algorithm

Propose using a hash map to store final capacities for open sites, initialized with their baseline values. Iterate through each closed site, and for each destination, add the additional capacity to the destination's total if it's open. Skip destinations that are closed.

3. Handle edge cases

Consider scenarios like a closed site pointing to another closed site (should be ignored), multiple closed sites pointing to the same destination (sum all), and closed sites with no destinations. Also, ensure that baseline capacities are correctly extracted from the self-referential key.

4. Implement and test

Write pseudocode or actual code, then walk through a small example to verify correctness. Test with edge cases like all sites closed, no closed sites, and chains of redistribution.

5. Analyze complexity

State that the time complexity is O(N + M) where N is the number of sites and M is the total number of redistribution entries, and space complexity is O(N) for the hash map. This shows efficiency.

Key Points to Mention

Data modeling: nested dictionary with self-referential keys for baseline capacity
Algorithm: iterate through closed sites and accumulate capacities into open destinations
Edge cases: closed sites pointing to closed sites, multiple contributions to same site, empty inputs
Output filtering: only include open sites in the final result
Time and space complexity analysis
Use of hash map for efficient lookups and updates

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