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 full Transformer block implementation question. Not a conceptual walkthrough, they wanted actual code plus a real conversation about why each piece exists. Pretty intense for a single session.

Questions Asked (1)

Implement a full Transformer encoder block from scratch in PyTorch or NumPy, including scaled dot-product attention, multi-head attention with Q/K/V and output projections, positional encoding, residual connections, LayerNorm, and the feed-forward sublayer. Be prepared to discuss time/space complexity and explain the role of each component.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This is the kind of question where you think you know Transformers until you have to actually write them line by line under pressure.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by outlining the overall architecture and data flow, then implement each component modularly with clear function signatures. Emphasize the purpose of each part and how they interact, and be ready to discuss complexity and design choices.

Pro tip: Write clean, vectorized code and mention that you would use PyTorch's built-in functions for efficiency, but you can also implement from scratch to demonstrate understanding. Also, proactively discuss numerical stability and masking.

1. Clarify requirements and outline architecture

Confirm the input/output shapes, batch size, sequence length, and model dimensions. Sketch the encoder block: input -> positional encoding -> multi-head attention -> add & norm -> feed-forward -> add & norm.

2. Implement scaled dot-product attention

Write a function that takes Q, K, V and optional mask, computes attention scores, scales by sqrt(d_k), applies softmax, and returns weighted sum. Discuss complexity O(n^2 d).

3. Implement multi-head attention

Split Q, K, V into multiple heads, apply scaled dot-product attention in parallel, concatenate heads, and apply output projection. Explain how this allows attending to different representation subspaces.

4. Add positional encoding, residual connections, and LayerNorm

Implement sinusoidal positional encoding and add to input embeddings. For each sublayer, apply residual connection followed by LayerNorm (post-norm or pre-norm). Explain their roles in stabilizing training and enabling deep networks.

5. Implement feed-forward sublayer and assemble block

Create a two-layer MLP with ReLU/GELU activation and dropout. Combine all components into a single encoder block class/function. Discuss parameter count and computational complexity.

Key Points to Mention

Scaled dot-product attention formula and why scaling by sqrt(d_k) is necessary to prevent softmax saturation.
Multi-head attention: splitting into heads, parallel computation, concatenation, and output projection.
Positional encoding: sinusoidal functions, why they are added, and how they provide sequence order information.
Residual connections and LayerNorm: how they mitigate vanishing gradients and stabilize training.
Feed-forward sublayer: expansion to higher dimension (e.g., 4x), activation function, and projection back.
Time and space complexity: O(n^2 d) for attention, O(n d^2) for feed-forward, and memory usage for intermediate activations.

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