Openai·Machine Learning Engineer·Take-home Assignment·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Took a take-home style ML engineering screen for OpenAI that was basically a full mini-project: build a classifier end-to-end, do the EDA, handle imbalance, tune models, explain your choices, and write it all up. Dense scope for a single assignment.

Questions Asked (1)

Given a labeled dataset, build a complete end-to-end classifier pipeline in Python: perform exploratory data analysis including schema validation, missing value handling, target leakage checks, class imbalance assessment, and feature distributions; create appropriate train/validation/test splits; define a baseline; train at least two models with cross-validation and hyperparameter tuning; handle class imbalance; apply preprocessing; evaluate with business-aligned metrics; run error analysis with feature importances or SHAP; check fairness across subgroups; and deliver a reproducible report with your recommended model.

Technical Trade-offsProduct Analytics & MetricsRoot Cause Analysis

Author's notes

This was the whole assignment, not just one question.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a narrative walkthrough of a real project, emphasizing decision points and trade-offs at each stage. Start with data understanding and validation, then move through modeling and evaluation, and end with actionable recommendations and reproducibility. Highlight how you align technical choices with business goals and fairness considerations.

Pro tip: Always connect technical steps to business impact—e.g., explain how you chose metrics based on cost of false positives vs. false negatives, and how fairness checks mitigate real-world harm. This shows you think like a product-minded ML engineer, not just a modeler.

1. Data Understanding and Validation

Perform EDA: check schema, missing values, target leakage, class imbalance, and feature distributions. Document data quality issues and their potential impact.

2. Data Splitting and Baseline

Create train/validation/test splits (stratified if needed) and establish a simple baseline (e.g., majority class or logistic regression) to set a performance benchmark.

3. Model Training and Tuning

Train at least two models (e.g., tree-based and linear) with cross-validation and hyperparameter tuning. Apply preprocessing (imputation, scaling, encoding) within pipelines to avoid leakage.

4. Evaluation and Error Analysis

Evaluate using business-aligned metrics (e.g., precision-recall AUC, F1, cost-sensitive). Handle class imbalance via resampling or class weights. Use feature importances or SHAP for error analysis.

5. Fairness, Reproducibility, and Recommendation

Check fairness across subgroups (e.g., demographic parity, equal opportunity). Ensure reproducibility with seeds, environment files, and documented steps. Deliver a report with recommended model and rationale.

Key Points to Mention

Target leakage detection: use correlation analysis, time-based splits if temporal, and domain knowledge to identify features that wouldn't be available at prediction time.
Class imbalance handling: choose between resampling (SMOTE, undersampling) and class weights, and justify based on metric sensitivity and computational cost.
Business-aligned metrics: map model errors to business costs (e.g., false negatives in fraud detection) and select metrics like precision@k or cost curves accordingly.
Cross-validation strategy: use stratified k-fold for imbalanced data, and nested CV for unbiased hyperparameter tuning if time permits.
Fairness assessment: define sensitive groups, compute metrics like disparate impact or equalized odds, and discuss mitigation strategies if disparities exist.
Reproducibility: set random seeds, use version control for data and code, and containerize the environment (e.g., Docker) for consistent results.

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