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Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Interviewed for an ML Engineer role at OpenAI and got hit with a multi-head attention implementation question from scratch. Pretty deep on the theory side, not just a vibe check.

Questions Asked (1)

Q1

Implement multi-head attention from scratch using NumPy or PyTorch.

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

I knew this was coming eventually but still fumbled the scaling factor for a second.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements (e.g., framework, input shapes, masking) and then implement scaled dot-product attention followed by multi-head attention. Structure your code modularly, explaining each component and its purpose, and discuss trade-offs and optimizations.

Pro tip: Demonstrate production awareness by mentioning how you would handle edge cases like masking, numerical stability, and efficient batching, and relate your implementation to real-world systems like Transformers.

1. Clarify Requirements and Set Up

Ask clarifying questions about the expected input dimensions, framework preference (NumPy or PyTorch), and whether masking or dropout is needed. Set up the necessary imports and define placeholder tensors.

2. Implement Scaled Dot-Product Attention

Write a function that computes attention scores as QK^T / sqrt(d_k), applies optional masking, and returns softmax-weighted values. Explain each operation and its purpose.

3. Implement Multi-Head Attention

Split the input into multiple heads by reshaping and transposing, apply scaled dot-product attention in parallel, concatenate the heads, and apply a final linear projection. Explain the benefits of multi-head attention.

4. Test and Validate

Run a quick test with random inputs to ensure the output shape matches expectations and the implementation is correct. Optionally, compare against a known implementation or use gradient checking.

5. Discuss Trade-offs and Optimizations

Talk about computational complexity, memory usage, and potential optimizations like using einsum, avoiding unnecessary transposes, and leveraging batch operations. Mention how this scales to large models.

Key Points to Mention

  • Scaled dot-product attention formula and why scaling by sqrt(d_k) is important for numerical stability.
  • The role of multiple heads in capturing diverse relationships and the need for linear projections for Q, K, V.
  • Handling of masking (e.g., padding mask, causal mask) and its impact on attention scores.
  • Efficient tensor operations: reshaping, transposing, and batch matrix multiplication.
  • Trade-offs between NumPy and PyTorch implementations (e.g., autograd, GPU support).
  • Complexity analysis: O(n^2 * d) time and memory, and strategies to mitigate (e.g., sparse attention).

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