Openai·Machine Learning Engineer·Technical Phone Screen·Senior

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Jul 2026

Summary

OpenAI ML Engineer interview with a meaty open-ended problem about noisy multi-annotator labels for binary text classification. One question but it sprawled into five or six sub-problems across analysis, training strategy, and evaluation design. Felt more like a whiteboard design session than a traditional interview.

Questions Asked (1)

You have a text dataset for binary classification where each example was labeled by multiple annotators who often disagree. Walk through how you'd analyze the label noise, build train/val/test splits, convert the multi-annotator labels into training targets, choose a model and loss function, and identify ways your offline metrics could be misleading.

Technical Trade-offsSystem DesignProduct Analytics & Metrics

Author's notes

This question wrecked me a little because I kept trying to answer each sub-part sequentially and the interviewer just kept layering more on top.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as one of modeling annotator behavior and uncertainty, not just aggregating labels. Walk through a systematic pipeline: analyze disagreement, design splits that prevent leakage, choose targets and loss that reflect uncertainty, and stress-test metrics for hidden pitfalls. Emphasize trade-offs and practical considerations at each stage.

Pro tip: Don't just default to majority vote; consider modeling annotator reliability (e.g., Dawid-Skene) and using soft labels or uncertainty-aware losses. Also, ensure your test set reflects the same annotator distribution as deployment to avoid overestimating performance.

1. Analyze label noise and annotator behavior

Compute inter-annotator agreement (e.g., Cohen's/Fleiss' kappa), per-annotator bias, and identify ambiguous examples. Visualize disagreement patterns to understand if noise is random or systematic.

2. Design train/val/test splits to avoid leakage

Split by example, not by annotator, to prevent the model from memorizing annotator-specific quirks. Ensure each split has similar disagreement rates and annotator coverage; consider stratifying by label and disagreement level.

3. Convert multi-annotator labels into training targets

Choose between hard labels (majority vote, weighted vote) and soft labels (empirical label distribution, probabilistic models like Dawid-Skene). Consider modeling annotator expertise and example difficulty.

4. Select model and loss function

Use a model that can output probabilities (e.g., logistic regression, neural network with sigmoid). Choose a loss that handles soft labels (e.g., cross-entropy with soft targets, KL divergence) or explicitly models annotator noise (e.g., noise-robust losses).

5. Identify misleading offline metrics

Check for metrics that ignore uncertainty (e.g., accuracy on majority vote), overfit to annotator bias, or fail under distribution shift. Evaluate calibration, per-annotator performance, and performance on high-disagreement subsets.

Key Points to Mention

Inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa) and their limitations
Soft labels vs. hard labels and their impact on model calibration
Dawid-Skene model or similar for estimating true labels and annotator reliability
Data leakage risks when splitting by annotator or example
Loss functions for noisy labels: cross-entropy with soft targets, noise-robust losses, or explicit noise modeling
Metrics pitfalls: accuracy on majority vote, ignoring calibration, and evaluating on a test set with different annotator distribution

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