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Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026Remote

Summary

Interviewed for an ML Engineer role at OpenAI. Two very dense technical questions, both requiring serious depth. Left feeling like I'd done okay on the first one but probably exposed some gaps on the from-scratch implementation part.

Questions Asked (2)

Q1

You're given a CIFAR-scale image dataset with label noise, corrupted samples, and class imbalance. How do you build a baseline classifier and a full data quality improvement plan, including detecting noise, filtering bad samples, handling imbalance, splitting data safely, and comparing mitigation strategies like confidence-based pruning, co-teaching, and augmentations like MixUp or CutMix?

Technical Trade-offsRoot Cause AnalysisA/B Testing & Experimentation
Author's notes

This one I actually felt decent about.

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AI HintsAI Generated

Suggested Approach

Start by establishing a clean, reproducible baseline with a simple model and stratified splits, then systematically diagnose data issues (noise, corruption, imbalance) using model-derived signals and data-centric methods. Propose a pipeline that detects and filters bad samples, applies imbalance mitigation, and compares advanced training strategies (confidence pruning, co-teaching, MixUp/CutMix) via controlled experiments with proper metrics.

Pro tip: Emphasize that you always validate data quality improvements on a small, manually verified clean holdout set to avoid overfitting to noisy validation metrics, and that you track both overall accuracy and per-class recall to catch hidden biases.

1. Establish a robust baseline and safe data splits

Train a simple model (e.g., ResNet-18) on a stratified split that preserves class distribution and isolates a clean validation set. Use cross-validation or a fixed holdout to ensure reliable evaluation.

2. Diagnose data quality issues

Analyze label noise via confusion matrices, loss distributions, and confident learning; detect corrupted samples via outlier detection or reconstruction error; quantify class imbalance with class frequencies and per-class performance.

3. Implement data cleaning and imbalance handling

Filter noisy/corrupted samples using confidence-based pruning or cleanlab, and address imbalance with class weighting, resampling, or focal loss. Re-evaluate on the clean holdout.

4. Compare advanced mitigation strategies

Experiment with co-teaching (two networks select clean samples for each other), MixUp/CutMix augmentations, and combinations. Use A/B testing with statistical significance to compare against baseline and simpler methods.

5. Iterate and monitor

Continuously monitor model performance on the clean holdout, adjust thresholds and strategies, and document trade-offs (e.g., computational cost vs. accuracy gain).

Key Points to Mention

  • Confident learning and cleanlab for noise detection
  • Stratified splitting and clean holdout for safe evaluation
  • Class weighting, resampling, and focal loss for imbalance
  • Co-teaching and small-loss selection for robust training
  • MixUp/CutMix for regularization and noise robustness
  • A/B testing framework with proper metrics (per-class recall, F1)

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

Q2

Using only NumPy-style linear algebra, implement forward and backward passes for a two-layer network with ReLU and softmax cross-entropy from scratch. Derive the gradients analytically, validate with numerical gradient checks, and discuss numerical stability, weight initialization, regularization, and how you'd extend this to a CNN architecture.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

Rougher than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining the network architecture and forward pass equations, then derive the gradients analytically for each layer. Implement the backward pass using these gradients, validate with numerical gradient checking, and discuss practical considerations like numerical stability, initialization, regularization, and CNN extensions.

Pro tip: Emphasize the importance of vectorized implementations and numerical stability tricks (e.g., log-sum-exp) to demonstrate production-level awareness. Also, mention that gradient checking should be done in double precision and with a small subset of parameters to avoid computational overhead.

1. Define Architecture and Forward Pass

Specify the two-layer network: input -> linear -> ReLU -> linear -> softmax. Write the forward pass equations in NumPy, including the softmax cross-entropy loss with numerical stability (subtract max logits).

2. Derive Gradients Analytically

Compute gradients of the loss w.r.t. parameters and inputs using backpropagation. For softmax cross-entropy, the gradient w.r.t. logits is (probs - one_hot). Then propagate through the second linear layer, ReLU, and first linear layer.

3. Implement Backward Pass

Code the backward pass using the derived gradients, ensuring vectorized operations. Cache intermediate values (e.g., activations, pre-activations) during forward pass for use in backward pass.

4. Validate with Numerical Gradient Check

Implement a numerical gradient checker using finite differences. Compare analytical gradients with numerical ones for a small random subset of parameters, ensuring relative error is below a threshold (e.g., 1e-7).

5. Discuss Practical Considerations and Extensions

Cover numerical stability (log-sum-exp, avoiding overflow), weight initialization (He initialization for ReLU), regularization (L2, dropout), and how to extend to CNNs (convolutional layers, pooling, and their backward passes).

Key Points to Mention

  • Numerical stability in softmax: subtract max logit before exponentiation to prevent overflow.
  • Weight initialization: He initialization for ReLU layers to maintain variance and avoid vanishing/exploding gradients.
  • Regularization: L2 regularization adds a penalty term to the loss and its gradient; dropout randomly zeroes activations during training.
  • Gradient checking: use central differences, double precision, and relative error; disable dropout during checking.
  • Vectorization: implement operations using matrix multiplications and broadcasting for efficiency.
  • CNN extension: convolutional layers share weights, so gradients involve summing over spatial locations; pooling layers have no parameters but need gradient routing.

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