Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

Technical interview for an ML Engineer role at OpenAI, focused entirely on debugging a broken Transformer implementation in PyTorch. One long coding problem, multiple bugs to find, and a lot of pressure to be systematic about it.

Questions Asked (1)

You're given a buggy Transformer implementation and a training script. The forward pass and training loss don't match a reference. How do you systematically find and fix all the issues?

Root Cause AnalysisTechnical Trade-offsSystem Design

Author's notes

This was the whole interview basically.

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AI HintsAI Generated

Suggested Approach

Start by reproducing the mismatch with a minimal, deterministic setup (fixed seed, small batch) and compare intermediate activations and gradients layer-by-layer against the reference. Then isolate and fix issues one at a time, validating each fix with unit tests and loss curves before moving on.

Pro tip: Before diving into code, write down the expected tensor shapes and mathematical operations for each component; many bugs are shape mismatches or incorrect broadcasting that a quick dimensional analysis reveals.

1. Reproduce and isolate

Create a minimal reproducible example with fixed random seeds and a small dataset. Confirm the mismatch exists and measure the exact discrepancy in loss and outputs.

2. Verify data and preprocessing

Check that input data, tokenization, masking, and labels are identical to the reference. Ensure no unintended shuffling or normalization differences.

3. Compare layer-by-layer

Instrument the model to dump intermediate activations and gradients. Compare each component (embeddings, attention, feed-forward, layer norm) against the reference implementation.

4. Fix and validate incrementally

Address one bug at a time, writing unit tests for each component. After each fix, re-run the comparison to ensure the loss curve converges toward the reference.

5. End-to-end verification

Once all components match, run the full training script and compare final metrics, convergence speed, and generalization to the reference.

Key Points to Mention

Deterministic debugging: setting seeds, disabling dropout, using small batches
Shape and broadcasting checks for tensors in attention and feed-forward layers
Correct implementation of attention masks (padding and causal) and their application
Proper initialization of weights and biases, especially for layer norm and residual connections
Gradient checking and numerical stability (e.g., softmax overflow, log-sum-exp)
Unit testing individual components against reference outputs

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