Openai·Machine Learning Engineer·Onsite - Coding / Algorithms·Senior

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Apr 2026

Summary

OpenAI MLE coding round centered on a broken Transformer implementation where you hunt down planted bugs until the training loop actually converges. There's a follow-up that's either a classifier head swap or a KV cache plug-in, and the whole thing moves fast.

Questions Asked (3)

You're given a mostly working Transformer implementation with several bugs planted in it. Find and fix all of them so that training loss converges and outputs are correct.

Algorithms & Data StructuresTechnical Trade-offsRoot Cause Analysis

Author's notes

This is the core exercise and it's less about memorizing a bug list than about actually understanding why each thing is broken.

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AI HintsAI Generated

Suggested Approach

Start by explaining your systematic debugging process: first verify the model can overfit a tiny batch, then inspect each component (attention, normalization, residuals, masking) against known correct implementations. Prioritize bugs that break gradient flow or numerical stability, and validate fixes with unit tests and loss curves.

Pro tip: Mention that you'd add assertions for shape and value ranges (e.g., attention weights sum to 1, no NaNs) and use gradient checking to catch subtle bugs early. This shows you build safeguards, not just fix symptoms.

1. Reproduce and isolate

Run the model on a small dataset and confirm the failure mode (e.g., loss not decreasing, NaNs). Use a tiny batch to see if the model can overfit, which quickly reveals fundamental bugs.

2. Inspect critical components

Review attention mechanism (scaling, masking), layer normalization (epsilon, axis), residual connections, and positional encoding for common mistakes like missing scaling or incorrect masking.

3. Check gradient flow and initialization

Verify that gradients are flowing to all parameters and that initialization (e.g., Xavier, Kaiming) is appropriate. Look for vanishing/exploding gradients or dead ReLUs.

4. Validate with unit tests

Write small tests for each component: attention output shapes, mask application, softmax sums to 1, and gradient checks. Compare against a reference implementation if possible.

5. Fix and verify convergence

Apply fixes one at a time, re-running the tiny-batch overfit test after each change. Finally, train on a larger dataset and monitor loss curves to ensure convergence.

Key Points to Mention

Attention scaling by 1/sqrt(d_k) to prevent softmax saturation
Correct masking for padding and causal attention (e.g., -inf before softmax)
Layer normalization placement (pre-norm vs post-norm) and epsilon
Residual connections and their role in gradient flow
Proper initialization (e.g., Xavier/Glorot) and its impact on training
Gradient checking and unit tests for component validation

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

Modify the Transformer to work as a classifier: replace the language model head with a classification head, adjust the prediction and loss accordingly. Some interviewers ask for mean-pooling over the sequence before the final projection.

Technical Trade-offsAlgorithms & Data Structures

Author's notes

Felt more straightforward than the bug hunt once I got there.

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AI HintsAI Generated

Suggested Approach

Start by outlining the standard Transformer architecture and its language modeling head, then explain how to replace it with a classification head. Discuss the necessary changes to the loss function and prediction process, and address the optional mean-pooling technique. Emphasize trade-offs and design choices.

Pro tip: Highlight that mean-pooling can be beneficial for tasks where all tokens contribute equally, but consider using the [CLS] token or attention pooling for tasks requiring a global representation. Mention that the choice depends on the specific task and dataset.

1. Understand the base Transformer

Recall that a Transformer for language modeling outputs a hidden state for each token and uses a linear layer to project to vocabulary logits. The loss is cross-entropy between predicted and actual next tokens.

2. Replace the language model head

Remove the vocabulary projection layer and add a classification head, typically a linear layer mapping the hidden size to the number of classes. This head can be applied to a pooled representation or to each token (for token-level classification).

3. Adjust prediction and loss

For sequence classification, aggregate token representations (e.g., mean-pooling, [CLS] token, or max-pooling) to get a fixed-size vector, then pass through the classification head to get class logits. Use cross-entropy loss between logits and true labels.

4. Consider mean-pooling

If mean-pooling is requested, compute the average of all token hidden states (excluding padding) before the final projection. Discuss its simplicity and potential drawbacks, such as losing positional or importance information.

5. Discuss trade-offs and alternatives

Compare mean-pooling with other pooling methods (e.g., using the [CLS] token, attention pooling, or max-pooling). Mention that the choice can affect performance and should be validated empirically.

Key Points to Mention

Transformer architecture: encoder/decoder stacks, self-attention, feed-forward layers
Language model head: linear layer to vocabulary size, softmax, cross-entropy loss
Classification head: linear layer to number of classes, softmax (or sigmoid for multi-label), cross-entropy loss
Pooling strategies: mean-pooling, [CLS] token, max-pooling, attention pooling
Handling padding: masking before pooling to avoid including padding tokens
Trade-offs: mean-pooling is simple but may dilute important signals; [CLS] token is learned but may not generalize; attention pooling adds parameters

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

Given a skeleton KV cache class, plug caching into the attention mechanism of the Transformer, handle positional embedding indexing correctly for cached steps, and wire up the pass-through parameters.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

If you've never implemented a KV cache before this will feel chaotic under time pressure.

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AI HintsAI Generated

Suggested Approach

Start by explaining the role of KV caching in autoregressive decoding and how it avoids redundant computation. Then, walk through the modifications needed in the attention mechanism: concatenating cached keys/values with new ones, adjusting positional indices for cached steps, and ensuring pass-through parameters (like attention mask and past length) are correctly propagated. Finally, discuss potential pitfalls and trade-offs.

Pro tip: Emphasize that positional embeddings must be offset by the cache length to maintain correct positional information, and that the attention mask must account for the cached sequence length to prevent attending to padding or future tokens.

1. Understand the KV Cache Skeleton

Review the provided KV cache class to understand its interface: methods for storing and retrieving keys/values, and how it tracks sequence length. Identify where to integrate it into the attention layer.

2. Modify Attention for Caching

In the attention forward pass, compute new keys and values, then concatenate them with cached ones along the sequence dimension. Use the updated keys/values for attention computation.

3. Handle Positional Embeddings

When computing positional embeddings for the new tokens, offset the positions by the current cache length. This ensures that each token gets the correct positional index relative to the full sequence.

4. Wire Pass-Through Parameters

Ensure that parameters like attention mask, past key/value lengths, and any other relevant arguments are correctly passed through to the attention function and used to shape the attention scores.

5. Validate and Discuss Trade-offs

Test the implementation with and without caching to ensure numerical equivalence. Discuss memory vs. speed trade-offs and potential issues like cache eviction policies.

Key Points to Mention

Concatenation of cached and new keys/values along the sequence dimension.
Offsetting positional indices by the cache length to maintain correct positional information.
Adjusting the attention mask to account for the cached sequence length (e.g., causal mask with past length).
Ensuring that the cache is updated with new keys/values after each forward pass.
Handling batch dimensions and variable sequence lengths in the cache.
Trade-offs between cache size, memory usage, and inference speed.

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