Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

OpenAI ML Engineer interview with a debugging-focused coding round. They handed me a broken Transformer classifier and wanted me to fix it, which sounds manageable until you realize how many subtle things can go wrong in a small model.

Questions Asked (1)

You're given a buggy Transformer-based text classifier. Find and fix the issues so it trains and predicts correctly.

Technical Trade-offsAlgorithms & Data StructuresRoot Cause Analysis

Author's notes

The part they really cared about was how you collapse the sequence of token embeddings into a single vector before the classification head.

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AI HintsAI Generated

Suggested Approach

Start by systematically reviewing the code from data loading through model definition, training loop, and prediction. Identify common bugs such as incorrect tensor shapes, missing normalization, wrong loss function, or improper masking. Then fix each issue, validate with a small dataset, and ensure the model trains and predicts correctly.

Pro tip: Always run a quick sanity check with a tiny dataset to catch shape mismatches and verify that the model can overfit a single batch before scaling up. This isolates bugs early and saves debugging time.

1. Inspect Data Pipeline

Check tokenization, padding, attention masks, and label alignment. Ensure inputs are correctly formatted and batched.

2. Review Model Architecture

Verify the Transformer layers, embedding dimensions, and output layer match the task (e.g., classification head). Look for missing components like positional encodings or dropout.

3. Examine Training Loop

Check loss function, optimizer, learning rate, and gradient updates. Ensure the model is in training mode and gradients are computed correctly.

4. Validate Prediction Logic

Inspect inference code for correct model evaluation mode, no gradient computation, and proper post-processing (e.g., softmax, argmax).

5. Test and Iterate

Run the fixed code on a small subset, monitor loss and accuracy, and debug any remaining issues until the model trains and predicts as expected.

Key Points to Mention

Attention mask handling to prevent padding tokens from affecting self-attention.
Correct loss function for classification (e.g., CrossEntropyLoss) and label formatting.
Proper initialization of weights and biases, especially for the classification head.
Learning rate scheduling and optimizer choice (e.g., AdamW) for stable training.
Model evaluation mode (model.eval()) and torch.no_grad() during inference.
Shape consistency between input embeddings, hidden states, and output logits.

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