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Tesla·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Tesla ML engineer screen, one technical question about conv layer parameter counts. Pretty focused, no fluff.

Questions Asked (1)

Q1

For a 2D convolution layer with given input channels, output channels, kernel dimensions, stride, padding, and an optional bias, derive the total number of learnable parameters both with and without bias.

Technical Trade-offsAlgorithms & Data Structures
Author's notes

I knew the formula but fumbled explaining it cleanly under pressure.

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AI HintsAI Generated

Suggested Approach

Start by defining the variables and explaining how each affects the parameter count. Then derive the formula step-by-step, clearly separating the weight and bias contributions, and finally present the total with and without bias.

Pro tip: Mention that the number of parameters is independent of the input spatial dimensions and stride/padding, which only affect the output size. This shows you understand the distinction between parameter count and computational cost.

1. Define variables

Clearly state the input channels (C_in), output channels (C_out), kernel height (K_h), kernel width (K_w), and whether bias is used.

2. Derive weight parameters

Each output channel has a kernel of size K_h x K_w for each input channel, so weights = C_out * C_in * K_h * K_w.

3. Account for bias

If bias is used, add one bias parameter per output channel, so bias parameters = C_out.

4. Compute total parameters

Total with bias = weights + bias; without bias = weights only. Present both formulas.

5. Discuss implications

Briefly mention how parameter count scales with channels and kernel size, and note that stride/padding do not affect it.

Key Points to Mention

  • Formula for weights: C_out * C_in * K_h * K_w
  • Bias adds C_out parameters (one per output channel)
  • Total parameters with bias: C_out * (C_in * K_h * K_w + 1)
  • Total parameters without bias: C_out * C_in * K_h * K_w
  • Stride and padding do not affect the number of learnable parameters
  • Parameter count is independent of input spatial dimensions (height and width)

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