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Openai·Software Engineer·Take-home Assignment·Senior

SeniorPrefer not to say
Jun 2026

Summary

Got a take-home style problem for a SWE role at OpenAI that was more data quality and ML evaluation than pure coding. Basically asked to write a mini pipeline to detect label noise and report metrics. No model training, just NumPy/Pandas and some clear thinking about thresholds.

Questions Asked (2)

Q1

Given binary classification predictions and ground truth labels, compute accuracy, precision, recall, F1, and ROC-AUC. Then explain why accuracy is a poor metric under class imbalance and suggest better alternatives.

Product Analytics & MetricsTechnical Trade-offs
Author's notes

The metrics part was fine, almost too straightforward.

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AI HintsAI Generated

Suggested Approach

First, walk through the computation of each metric from the confusion matrix, clearly defining TP, FP, FN, TN. Then, explain why accuracy is misleading under class imbalance, using a concrete example, and discuss better alternatives like precision, recall, F1, and ROC-AUC, highlighting their trade-offs.

Pro tip: Mention that ROC-AUC can also be misleading under extreme imbalance and that precision-recall AUC is often more informative. This shows depth beyond the basics.

1. Define the confusion matrix

Start by defining TP, FP, FN, TN from the predictions and ground truth. This sets the foundation for all metric calculations.

2. Compute accuracy, precision, recall, F1

Show the formulas and compute each metric. Explain what each represents: accuracy is overall correctness, precision is positive predictive value, recall is sensitivity, and F1 is the harmonic mean of precision and recall.

3. Compute ROC-AUC

Explain that ROC-AUC measures the model's ability to distinguish between classes across all thresholds. If probabilities are available, describe how to compute it; otherwise, mention it requires scores.

4. Explain accuracy's pitfalls under imbalance

Use an example: if 99% of data is negative, a model predicting all negative gets 99% accuracy but fails to identify positives. Accuracy ignores the cost of false negatives and class distribution.

5. Suggest better alternatives

Recommend precision, recall, F1, ROC-AUC, and precision-recall AUC. Discuss when to use each: e.g., recall when false negatives are costly, precision when false positives are costly, F1 for balance, and PR-AUC for extreme imbalance.

Key Points to Mention

  • Confusion matrix components: TP, FP, FN, TN
  • Formulas for accuracy, precision, recall, F1, and ROC-AUC
  • Accuracy paradox: high accuracy can be misleading when classes are imbalanced
  • Precision vs. recall trade-off and the role of F1 score
  • ROC-AUC interpretation and its limitations under severe imbalance
  • Precision-Recall AUC as a better alternative for imbalanced datasets

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

Q2

Design a label-noise detection method that flags samples where the model is highly confident but disagrees with the label. Output the suspect indices, and then propose a workflow to validate and clean those samples.

Root Cause AnalysisProduct Analytics & MetricsAlgorithms & Data Structures
Author's notes

This was the more interesting half.

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AI HintsAI Generated

Suggested Approach

Start by defining a confidence threshold and a disagreement criterion to flag suspect samples, then output their indices. Next, propose a validation workflow that includes manual review, cross-validation with a clean subset, and iterative model retraining to clean the dataset.

Pro tip: Emphasize the importance of validating flagged samples with domain experts and using a small, high-confidence clean set to estimate the false positive rate of your detection method.

1. Define detection criteria

Set a confidence threshold (e.g., predicted probability > 0.9) and flag samples where the model's prediction disagrees with the given label. Output the indices of these suspect samples.

2. Prioritize and sample for review

Rank suspect samples by confidence or margin, and select a subset for manual inspection to validate the flags efficiently.

3. Validate with multiple signals

Use cross-validation with a clean subset, ensemble disagreement, or human annotation to confirm whether the label is indeed noisy.

4. Clean and retrain

Correct or remove confirmed noisy labels, then retrain the model and monitor performance improvements to ensure the cleaning helped.

5. Iterate and monitor

Repeat the detection and cleaning process periodically, and track metrics like label noise rate and model accuracy to maintain data quality.

Key Points to Mention

  • Confidence threshold selection and its trade-offs (precision vs. recall)
  • Handling class imbalance and its effect on confidence scores
  • Using a small clean validation set to estimate false positive rate
  • Leveraging ensemble methods or cross-validation for robust detection
  • Human-in-the-loop validation for high-stakes or ambiguous cases
  • Iterative retraining and monitoring to avoid overfitting to noisy labels

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