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

SeniorPrefer not to say
May 2026

Summary

Interviewed for an ML engineer role at Luma AI and got hit with a low-level systems question that felt pretty out of left field for the position.

Questions Asked (1)

Q1

Implement an image copy operation from scratch, without using any external libraries.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Not what I was expecting from an ML interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the image format and constraints (e.g., in-memory buffer, raw pixel data, dimensions). Then outline a step-by-step algorithm for copying pixel data, considering memory layout, data types, and performance. Finally, discuss trade-offs such as row-by-row vs. bulk copy, handling different color channels, and potential optimizations like using memory views or parallelization.

Pro tip: Demonstrate awareness of real-world constraints: mention that in production you'd use optimized libraries (e.g., PIL, OpenCV) but implementing from scratch shows understanding of memory and performance. Also, proactively discuss edge cases like non-contiguous memory or padding.

1. Clarify requirements and constraints

Ask about the image format (e.g., raw RGB, grayscale), dimensions, data type (uint8, float32), and whether the source and destination are in memory or need I/O. Confirm if any libraries are allowed for basic types.

2. Design the data structure

Represent the image as a 2D or 3D array (height, width, channels) using a flat list or array module. Consider row-major vs. column-major layout and how to handle padding or alignment.

3. Implement the copy algorithm

Write a function that iterates over each pixel (or row) and copies values from source to destination. Use nested loops or slicing for efficiency, ensuring correct indexing for multi-channel data.

4. Optimize and handle edge cases

Discuss optimizations like copying row-by-row using slice assignment, using memoryview for zero-copy, or parallelizing with threads. Handle cases like non-contiguous memory, different strides, or alpha channels.

5. Test and validate

Mention writing unit tests to verify pixel-perfect copies, including edge cases like 1x1 images, large images, and different channel counts. Compare performance with built-in methods.

Key Points to Mention

  • Memory layout and row-major order: explain how images are stored and why contiguous memory matters for performance.
  • Data types and channels: discuss handling uint8 vs. float32, and RGB vs. RGBA, including potential need for type conversion.
  • Performance trade-offs: compare naive nested loops vs. row-wise slice copying vs. using memoryview or array module for speed.
  • Edge cases: non-contiguous source (e.g., from a crop), padding, and handling of alpha channel or grayscale.
  • Testing strategy: how to verify correctness (e.g., checksum, pixel comparison) and benchmark performance.
  • Real-world context: acknowledge that in production you'd use optimized libraries, but implementing from scratch demonstrates low-level understanding.

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