Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

OpenAI Research Engineer interview that dropped a buggy Transformer implementation in front of you and said 'find the problems.' There was also a follow-up where you had to convert the model into a classifier and run it against a test harness, so the code actually had to work, not just look right on a whiteboard.

Questions Asked (2)

You're given a hand-written Transformer implementation with four bugs in it. Find all four, fix them, and explain what each one was doing wrong.

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This is the kind of question that sounds manageable until you're actually staring at someone else's code and second-guessing every line.

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AI HintsAI Generated

Suggested Approach

Start by reading the code top-to-bottom to understand the overall architecture, then systematically check each component against the canonical Transformer design. Prioritize bugs that break fundamental operations (e.g., attention, normalization) and fix them one at a time, explaining the impact of each. Finally, verify the fixes by tracing through a simple example or running a unit test.

Pro tip: Demonstrate deep understanding by not only fixing the bugs but also explaining how each would manifest during training (e.g., loss not decreasing, NaNs) and how you would write a test to catch it. This shows you think like a debugging engineer, not just a coder.

1. Understand the code structure

Skim the entire implementation to identify the main components (e.g., attention, feed-forward, layer norm, positional encoding) and their intended functionality. This helps you spot deviations from the standard Transformer architecture.

2. Check attention mechanism

Verify the scaled dot-product attention: ensure queries, keys, and values are correctly projected, scaled by sqrt(d_k), masked appropriately, and softmaxed. Common bugs include missing scaling, incorrect masking, or wrong dimension permutation.

3. Inspect normalization and residual connections

Confirm that layer normalization is applied correctly (e.g., over the right dimension) and that residual connections are present around each sub-layer. Bugs here can cause training instability or vanishing gradients.

4. Validate feed-forward and positional encoding

Check the feed-forward network (two linear layers with activation) and positional encoding (e.g., sinusoidal or learned). Look for missing activation, incorrect dimensions, or off-by-one errors in positional indices.

5. Fix and explain each bug

For each identified bug, describe what it was doing wrong, why it matters, and how you fixed it. Optionally, suggest a test to prevent regression.

Key Points to Mention

Scaled dot-product attention: scaling by 1/sqrt(d_k) to prevent softmax saturation
Masking: ensuring causal mask for decoder self-attention and padding mask for all attention layers
Layer normalization: applying over the feature dimension and placing it correctly (pre-norm vs post-norm)
Residual connections: adding input to sub-layer output to facilitate gradient flow
Feed-forward network: two linear layers with ReLU/GELU activation in between
Positional encoding: adding positional information to embeddings, often using sinusoidal functions

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

Now convert that Transformer model into a classifier by swapping out the final layer, and make sure the code runs end-to-end against the provided test harness.

Technical Trade-offsAPI & Integrations

Author's notes

The follow-up felt almost harder than the debugging part because now the code had to actually execute and pass tests.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the model architecture and the test harness requirements, then outline the steps to replace the final layer with a classification head. Emphasize writing clean, modular code that integrates seamlessly with the provided harness, and discuss potential pitfalls like dimension mismatches and training stability.

Pro tip: Before coding, inspect the test harness to understand expected input/output formats and any constraints; this prevents integration issues and demonstrates thoroughness. Also, consider using a small learning rate for the new head to avoid disrupting pretrained weights.

1. Understand the Model and Harness

Review the Transformer model architecture and the test harness to identify the final layer, input/output shapes, and how the harness invokes the model.

2. Design the Classification Head

Decide on the classification head structure (e.g., linear layer with dropout) and ensure it matches the number of classes and the model's hidden dimension.

3. Implement the Modification

Replace the final layer with the new head, ensuring proper initialization and that the forward pass remains compatible with the harness.

4. Test End-to-End

Run the modified model against the test harness, debug any shape or runtime errors, and verify that the model produces expected outputs.

5. Discuss Trade-offs and Improvements

Mention considerations like freezing pretrained layers, handling class imbalance, and potential performance impacts.

Key Points to Mention

Identifying the correct layer to replace (e.g., the final linear projection or the [CLS] token output).
Ensuring the new classification head matches the expected output dimensions and the harness's input requirements.
Proper weight initialization for the new layer to avoid vanishing/exploding gradients.
Strategies for fine-tuning, such as freezing the Transformer body or using discriminative learning rates.
Handling potential issues like variable sequence lengths or padding masks in the classification head.
Testing with the harness early and often to catch integration issues.

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