luma ai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Interviewed for an MLE role at Luma AI and got a coding question that was a spin on a classic matrix problem. Not much context given about the round or outcome.

Questions Asked (1)

Given a 2D image represented as a matrix, write a function to transpose it. Note: this is a variant of the standard problem, not the exact LeetCode version.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I've done the LeetCode transpose before so my first instinct was to just write that solution.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., square vs. non-square matrix, in-place vs. out-of-place) and discuss trade-offs. Then outline an efficient algorithm, such as using extra space for non-square matrices or in-place swapping for square matrices, and analyze time and space complexity. Finally, relate the solution to ML applications like image preprocessing or data augmentation.

Pro tip: Demonstrate awareness of memory layout and cache efficiency: for large images, transposing in-place or using blocked algorithms can significantly improve performance, which is crucial in ML pipelines.

1. Clarify requirements and constraints

Ask whether the matrix is square or rectangular, and whether the transpose should be done in-place or can use extra memory. Also confirm the data type and size expectations.

2. Choose an approach and discuss trade-offs

For square matrices, propose in-place swapping; for rectangular, propose allocating a new matrix. Discuss time and space complexity of each.

3. Write pseudocode or code

Implement the chosen approach clearly, handling edge cases like empty matrices or 1xN matrices. Use appropriate loops and indexing.

4. Analyze complexity and optimize

State O(m*n) time and O(1) or O(m*n) space depending on approach. Mention potential optimizations like cache-friendly access patterns.

5. Relate to ML context

Explain how matrix transpose is used in ML, e.g., in image transformations, tensor operations, or data preprocessing, and why efficiency matters.

Key Points to Mention

Time complexity O(m*n) and space complexity O(1) for in-place square transpose vs. O(m*n) for rectangular.
In-place transpose for square matrices using swapping across the diagonal.
Handling non-square matrices by allocating a new matrix of size n x m.
Cache efficiency and memory access patterns, especially for large images.
Edge cases: empty matrix, 1xN matrix, and non-rectangular inputs.
Relevance to ML: image preprocessing, data augmentation, and tensor manipulations.

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