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Apple·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Apple ML engineer round, one meaty coding problem centered on image processing. Not a lot of fluff, they just dropped the problem and expected you to run with it.

Questions Asked (1)

Q1

Implement a 2D image convolution filter from scratch. Given an image (grayscale or RGB) and a kernel of arbitrary size, apply the kernel at every pixel and return a filtered image of the same dimensions. Handle boundary pixels and discuss complexity and potential optimizations.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

I started with the naive nested loop approach, which is fine, but I spent too long on the boundary padding discussion before writing any code.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: input format (grayscale or RGB), kernel size, and boundary handling. Then outline a naive implementation with nested loops, discuss complexity, and propose optimizations like separable kernels or vectorization. Finally, address boundary strategies and trade-offs.

Pro tip: Mention that for large kernels, using separable kernels (if applicable) or FFT-based convolution can drastically reduce complexity, but always consider the constant factors and memory overhead. Also, emphasize the importance of handling boundaries correctly to avoid artifacts.

1. Clarify Requirements and Assumptions

Ask about image format (grayscale/RGB), kernel size, boundary handling preference, and output requirements. Confirm if kernel is separable or symmetric.

2. Design Naive Algorithm

Describe a straightforward implementation: for each pixel, iterate over the kernel, multiply and sum. Handle boundaries by padding (zero, replicate, mirror) or cropping.

3. Analyze Complexity and Trade-offs

State time complexity O(H*W*K^2) for naive, and space O(H*W). Discuss how boundary handling affects complexity and memory.

4. Propose Optimizations

Suggest optimizations: separable kernels (O(K) per pixel), FFT-based convolution (O(HW log(HW))), vectorization (SIMD), parallelization, and using integral images for box filters.

5. Discuss Implementation Details

Mention data layout (row-major), memory access patterns, and how to handle multi-channel images. Consider edge cases like 1x1 kernel or large kernels.

Key Points to Mention

  • Boundary handling strategies: zero-padding, replicate, mirror, and their effects on output.
  • Time complexity: O(H*W*K^2) for naive, O(H*W*K) for separable, O(HW log(HW)) for FFT.
  • Separable kernels: when kernel is rank-1, apply two 1D convolutions for efficiency.
  • Optimizations: SIMD, multithreading, GPU acceleration, and memory locality.
  • Multi-channel handling: apply convolution per channel or use 3D kernel.
  • Trade-offs: accuracy vs speed, memory vs computation, and implementation complexity.

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