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Lila·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Lila gave me a live code review round where they handed me a full Transformer implementation and asked me to find what was broken. Brutal but fair, honestly the most technically dense interview I've done for an MLE role.

Questions Asked (1)

Q1

You're given a from-scratch Transformer implementation in a GitHub repo covering multi-head attention, positional encoding, layer norm, residuals, FFN, and a training loop. Find the bugs, explain what each one breaks, and show the corrected code.

System DesignTechnical Trade-offsAlgorithms & Data Structures
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AI HintsAI Generated

Suggested Approach

Start by outlining a systematic debugging strategy: first verify the overall architecture and data flow, then inspect each component against the original Transformer paper, and finally run targeted tests to confirm bugs and fixes. For each bug, explain its impact on training dynamics and model performance, and provide corrected code snippets.

Pro tip: Demonstrate deep understanding by not only fixing bugs but also explaining how each bug would manifest during training (e.g., loss not decreasing, gradients exploding) and how you would detect them using tools like gradient checking or logging attention weights.

1. Understand the architecture and data flow

Review the code structure to ensure you grasp how data flows through the model, from input embeddings to output logits, and identify all components mentioned.

2. Inspect each component against the paper

Compare the implementation of multi-head attention, positional encoding, layer norm, residuals, and FFN with the original 'Attention Is All You Need' paper to spot deviations.

3. Identify and explain bugs

For each bug found, articulate what it breaks (e.g., incorrect scaling in attention, missing residual connections) and how it affects training and model performance.

4. Provide corrected code

Show the corrected code snippets for each bug, ensuring they align with best practices and the original architecture.

5. Validate fixes with tests

Describe how you would test the fixes, such as running a small training loop to see if loss decreases or using gradient checks to ensure proper backpropagation.

Key Points to Mention

  • Scaled dot-product attention: missing scaling factor (1/sqrt(d_k)) leading to vanishing gradients.
  • Multi-head attention: incorrect splitting or concatenation of heads, or missing output projection.
  • Positional encoding: incorrect implementation (e.g., using learned embeddings instead of sinusoidal, or wrong frequency).
  • Layer normalization: applied incorrectly (e.g., before residual instead of after, or wrong dimension).
  • Residual connections: missing or incorrectly placed, causing degradation in deep networks.
  • Feed-forward network: missing activation function or incorrect dimensions.
  • Training loop: issues like incorrect learning rate schedule, missing gradient clipping, or improper masking.

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