Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

OpenAI ML interview that went deep on noisy label problems, which I wasn't fully prepped for. The question covered a lot of ground and felt more like a mini case study than a single question.

Questions Asked (1)

You have a text dataset for binary classification where multiple annotators have labeled each example and their labels frequently disagree. How would you analyze the dataset, aggregate the noisy labels, train a model, and evaluate whether your improvements are real?

Product Analytics & MetricsTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This one sprawled in a way I didn't expect.

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AI HintsAI Generated

Suggested Approach

Start by quantifying annotator disagreement and reliability, then use a principled aggregation method like Dawid-Skene to infer true labels and estimate annotator quality. Train a model on the aggregated labels with noise-robust techniques, and evaluate using a held-out set with expert labels or through cross-validation with simulated noise to ensure improvements are real.

Pro tip: Always compare against a simple majority vote baseline and report confidence intervals; this demonstrates rigor and helps distinguish genuine improvements from noise.

1. Analyze annotator disagreement

Compute inter-annotator agreement metrics (e.g., Cohen's kappa, Fleiss' kappa) and visualize confusion patterns to understand label noise structure.

2. Aggregate noisy labels

Apply a probabilistic model like Dawid-Skene or MACE to estimate true labels and annotator accuracies, or use majority voting as a baseline.

3. Train a noise-robust model

Use the aggregated labels to train a classifier, optionally incorporating noise-aware loss functions or co-teaching to handle residual noise.

4. Evaluate with reliable ground truth

Hold out a subset with expert consensus labels or use cross-validation with simulated noise to measure true performance and compare against baselines.

5. Validate improvements statistically

Perform significance testing (e.g., bootstrap or paired tests) on evaluation metrics to confirm that gains are not due to chance.

Key Points to Mention

Inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa)
Dawid-Skene model or other probabilistic label aggregation methods
Noise-robust training techniques (e.g., co-teaching, loss correction)
Importance of a clean validation set for unbiased evaluation
Statistical significance testing to validate improvements
Baseline comparison with majority vote

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