Tesla·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Tesla ML engineer interview, technical screen focused entirely on low-level NumPy implementation. No fluff, just code and tradeoffs. The kind of round where you really feel the gap between knowing something conceptually and actually writing it out.

Questions Asked (1)

Implement a 2D convolution from scratch using only NumPy, supporting NCHW input format, configurable stride and padding, and weights of shape (C_out, C_in, k_h, k_w). Start with a nested-loop reference implementation, then optimize it using vectorization (like im2col or stride tricks), and explain the time and memory tradeoffs between the two approaches.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This took me way longer than I expected to set up correctly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and then implement a straightforward nested-loop convolution to establish correctness. Next, optimize using im2col or stride tricks, explaining how they trade memory for speed. Finally, discuss the time and memory tradeoffs, including when each approach is preferable.

Pro tip: Mention that while im2col is common, stride tricks can be more memory-efficient for certain cases, and always validate with a small test case against a known implementation like PyTorch.

1. Clarify requirements and edge cases

Confirm input shapes, padding, stride, and output dimensions. Discuss handling of non-divisible strides and padding values.

2. Implement nested-loop reference

Write a clear, correct implementation using nested loops over output positions and channels. Use NumPy for basic operations but avoid vectorization.

3. Optimize with im2col or stride tricks

Explain and implement im2col to transform input into a matrix, then use matrix multiplication. Alternatively, use as_strided for a view-based approach.

4. Analyze time and memory tradeoffs

Compare the computational complexity and memory usage of both methods. Discuss when each is appropriate based on input size and hardware.

5. Validate and discuss extensions

Test against a known implementation (e.g., PyTorch) and mention potential further optimizations like FFT or Winograd.

Key Points to Mention

Output dimension formula: H_out = (H_in + 2*padding - k_h) / stride + 1
im2col memory overhead: expands input by factor of k_h * k_w
Stride tricks using as_strided can avoid copying but require careful handling of strides and memory layout
Time complexity: O(N * C_out * C_in * H_out * W_out * k_h * k_w) for both, but vectorization improves constant factors
Memory tradeoff: im2col uses more memory but enables efficient BLAS calls; stride tricks use less memory but may be slower due to non-contiguous access
Edge cases: padding with zeros, stride > 1, and non-square kernels

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