Openai·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

OpenAI SWE interview with a transformer debugging problem, which was actually pretty fair since they pointed you toward the buggy sections. There was a follow-up requiring classification-specific changes to the prediction and loss logic.

Questions Asked (2)

Debug a transformer implementation where the buggy code segments are pre-marked for you.

Technical Trade-offsAlgorithms & Data Structures

Author's notes

The fact that they flagged which parts had bugs made it way less stressful than I expected.

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AI HintsAI Generated

Suggested Approach

Start by understanding the expected behavior of the transformer and the purpose of each marked code segment. Then systematically debug each segment by tracing data flow, checking tensor shapes, and verifying mathematical operations against the transformer architecture. Finally, explain your fixes and discuss potential trade-offs or optimizations.

Pro tip: Verbalize your debugging process step-by-step, as if pair programming, to demonstrate clear reasoning and familiarity with transformer internals. Also, proactively mention edge cases like masking and numerical stability, which are common pitfalls.

1. Clarify expected behavior and architecture

Confirm the transformer variant (e.g., encoder-decoder, decoder-only) and the expected input/output shapes. Identify the role of each marked segment in the overall computation graph.

2. Inspect each marked segment for common bugs

Check for shape mismatches, incorrect matrix multiplications, missing transposes, wrong scaling factors, and improper masking. Verify attention score computation, softmax, and layer normalization.

3. Trace data flow and validate intermediate results

Use small example inputs to manually compute expected outputs for each segment. Compare with actual outputs to isolate the bug. Check gradient flow if training is involved.

4. Propose and justify fixes

For each bug, explain the correct implementation and why it fixes the issue. Discuss potential side effects and how to test the fix.

5. Discuss trade-offs and optimizations

Mention alternative implementations (e.g., using built-in functions vs. manual computation) and their impact on performance, readability, and numerical stability.

Key Points to Mention

Tensor shape compatibility and broadcasting rules
Attention mechanism details: query-key dot product, scaling, masking, softmax
Positional encoding implementation and its interaction with embeddings
Layer normalization and residual connections placement
Masking for padding and causal attention (if applicable)
Numerical stability techniques (e.g., softmax max subtraction, layer norm epsilon)

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

Modify the transformer code to handle a classification task, including updating the prediction head and loss function.

Technical Trade-offsSystem Design

Author's notes

Follow-up to the debugging part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the classification task (e.g., number of classes, input format) and the transformer architecture. Then, systematically describe the modifications: replace the prediction head with a linear layer, adjust the loss function to cross-entropy, and ensure the output dimensions match the number of classes. Finally, discuss potential trade-offs and validation steps.

Pro tip: Mention that you would freeze the transformer backbone initially and only train the new head, then fine-tune the whole model if needed—this shows practical experience with transfer learning and resource efficiency.

1. Clarify the Task and Architecture

Ask about the specific classification task (e.g., binary vs. multi-class), dataset size, and the transformer variant (e.g., BERT, GPT). Confirm the input representation (e.g., [CLS] token, pooled output).

2. Modify the Prediction Head

Replace the existing output layer (e.g., language modeling head) with a new linear layer that maps the hidden size to the number of classes. Optionally add a dropout layer for regularization.

3. Update the Loss Function

Switch from the original loss (e.g., cross-entropy for LM) to a classification loss such as cross-entropy loss (for single-label) or binary cross-entropy with logits (for multi-label). Ensure the output layer matches the loss (e.g., no softmax before cross-entropy).

4. Adjust Training and Evaluation

Update metrics (e.g., accuracy, F1) and consider freezing the backbone initially. Discuss hyperparameter tuning (learning rate, batch size) and validation strategy.

5. Discuss Trade-offs and Alternatives

Mention trade-offs like full fine-tuning vs. feature extraction, impact on inference latency, and alternatives like using a pooled output vs. [CLS] token. Highlight any potential pitfalls (e.g., class imbalance).

Key Points to Mention

Choice of pooling strategy (e.g., [CLS] token, mean pooling) for classification.
Replacing the head with a linear layer of size hidden_dim x num_classes.
Using cross-entropy loss for single-label classification and binary cross-entropy for multi-label.
Freezing the transformer backbone initially to reduce training cost and prevent overfitting.
Handling class imbalance with weighted loss or resampling.
Evaluating with appropriate metrics (accuracy, F1, AUC) and validation splits.

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