Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta ML Engineer interview with a grid-based coding problem that looked straightforward but had enough depth to keep me on my toes for a while. Two parts to implement plus complexity analysis and a discussion about recursion limits on large inputs.

Questions Asked (3)

Given an m x n character grid and a target character, implement a function that returns the size of the 4-directionally connected region containing a given cell if that cell matches the target character, and 0 otherwise.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Pretty standard flood-fill territory but I overthought the base case.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a depth-first search (DFS) or breadth-first search (BFS) solution to explore the connected region. Discuss trade-offs between iterative and recursive approaches, and analyze time and space complexity.

Pro tip: Mention that you would use an iterative BFS to avoid recursion depth limits, and that you can mutate the grid in-place to mark visited cells, saving space.

1. Clarify requirements and edge cases

Confirm the definition of 4-directional connectivity, handle cases where the starting cell is out of bounds or doesn't match the target, and discuss grid size limits.

2. Choose traversal algorithm

Select BFS or DFS based on trade-offs: BFS for shortest path or avoiding recursion limits, DFS for simplicity. Consider iterative vs recursive implementations.

3. Implement traversal with visited tracking

Use a queue (BFS) or stack (DFS) to explore neighbors, marking visited cells either with a separate set or by modifying the grid in-place.

4. Count region size and return result

Increment a counter for each visited cell that matches the target, and return the count if the starting cell matches, else 0.

5. Analyze complexity and discuss optimizations

State O(m*n) time and space complexity, and mention potential optimizations like early termination if only size is needed.

Key Points to Mention

4-directional connectivity (up, down, left, right)
BFS vs DFS trade-offs (recursion depth, memory usage)
In-place marking vs separate visited set
Time and space complexity analysis
Edge cases: out-of-bounds start, non-matching start, empty grid
Potential for parallelization or union-find for multiple queries

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

Using the same grid setup, implement a function that counts all distinct 4-directionally connected regions of a given character across the entire grid.

Algorithms & Data StructuresSystem Design

Author's notes

This one felt easier once the first part was done.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem: count connected components of a specific character in a 2D grid using 4-directional adjacency. Then implement a traversal (DFS or BFS) that marks visited cells and increments a counter for each new component. Discuss time and space complexity and consider edge cases like empty grid or no matching characters.

Pro tip: Mention that you can optimize space by modifying the grid in-place (e.g., changing visited cells to a sentinel) if mutation is allowed, but always ask the interviewer first. Also, highlight that BFS avoids recursion depth issues for large grids, which is important in production ML pipelines.

1. Clarify requirements and constraints

Confirm the grid dimensions, whether the grid can be modified, and if diagonal connections count. Ask about the expected input size to choose between DFS and BFS.

2. Choose traversal strategy

Decide between DFS (recursive or iterative) and BFS. For large grids, iterative BFS with a queue is safer to avoid stack overflow; for simplicity, recursive DFS is fine if depth is limited.

3. Implement component counting

Iterate over each cell; when encountering the target character and not visited, increment the count and launch a traversal to mark all connected cells as visited.

4. Handle edge cases and validate

Test with empty grid, grid with no target character, single cell, and fully connected grid. Ensure visited tracking works correctly.

5. Analyze complexity and optimize

State time complexity O(R*C) and space complexity O(R*C) for visited set or O(min(R,C)) for BFS queue in worst case. Discuss potential in-place modification to reduce space.

Key Points to Mention

Use a visited matrix or in-place marking to avoid revisiting cells.
4-directional means only up, down, left, right neighbors.
Time complexity is O(R*C) since each cell is visited once.
Space complexity depends on traversal: O(R*C) for visited set, O(min(R,C)) for BFS queue in worst case.
Edge cases: empty grid, no target character, all cells same character.
Choice between DFS and BFS: DFS simpler but recursion depth risk; BFS safer for large grids.

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

Analyze the time and space complexity of your solution, and explain how you'd handle recursion depth limit issues for worst-case grid inputs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The recursion depth question is where things got interesting.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clearly stating the time and space complexity of your solution in terms of grid dimensions (e.g., O(m*n) time, O(m*n) space), then explain how you would mitigate recursion depth issues by converting recursion to iteration or using an explicit stack. Finally, discuss trade-offs and mention any optimizations like tail recursion or increasing recursion limit with caution.

Pro tip: Show awareness of Python's default recursion limit and the risk of stack overflow on large grids; mentioning that you'd use an iterative DFS/BFS or set a higher recursion limit with sys.setrecursionlimit demonstrates practical maturity.

1. State Complexity

Clearly articulate the time and space complexity of your solution, specifying variables (e.g., m, n for grid dimensions) and whether it's optimal.

2. Identify Recursion Depth Issue

Explain that for worst-case grids (e.g., all cells connected in a line), recursion depth can reach O(m*n), potentially exceeding the call stack limit.

3. Propose Mitigation Strategies

Describe approaches to handle recursion depth: converting to iterative with explicit stack, using BFS, or increasing recursion limit (with caveats).

4. Discuss Trade-offs

Compare recursive vs. iterative solutions in terms of code clarity, memory usage, and performance, and justify your choice.

5. Conclude with Best Practice

Summarize the recommended approach for production code, emphasizing robustness and scalability.

Key Points to Mention

Time complexity: O(m*n) for grid traversal, where m and n are dimensions.
Space complexity: O(m*n) for visited set and recursion stack in worst case.
Recursion depth can reach O(m*n) in worst-case (e.g., snake-like path).
Python's default recursion limit (~1000) may be exceeded; sys.setrecursionlimit can be raised but risks stack overflow.
Iterative DFS with explicit stack or BFS with queue avoids recursion depth issues.
Trade-offs: iterative solutions may be more verbose but are safer for large inputs.

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