Openai·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

ML coding round at OpenAI for a machine learning engineer role. They had me build a classifier from scratch, which sounds manageable until you're live-coding a training loop and someone's watching every keystroke.

Questions Asked (1)

Implement a classifier from scratch (logistic regression or a small neural network), including the loss function, gradient updates, training loop, and model evaluation.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with logistic regression because I figured it'd be cleaner to reason about under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and assumptions (dataset, features, binary/multiclass, evaluation metric). Then outline the model architecture, loss function, and gradient derivation, and describe the training loop with optimization details. Finally, discuss evaluation metrics, validation strategy, and potential pitfalls.

Pro tip: Demonstrate production awareness by mentioning numerical stability techniques (e.g., log-sum-exp for softmax, gradient clipping) and how you would scale the implementation (vectorization, mini-batch, GPU).

1. Clarify Requirements and Assumptions

Ask about dataset size, feature types, number of classes, and evaluation metric. State assumptions if not specified (e.g., binary classification, dense features).

2. Define Model and Loss

Describe the model (logistic regression or small MLP), the forward pass, and the loss function (binary cross-entropy or categorical cross-entropy). Explain why this loss is appropriate.

3. Derive Gradients and Update Rule

Show the gradient of the loss w.r.t. parameters (e.g., for logistic regression: grad = (sigmoid(z) - y) * x). For neural network, use backpropagation. Specify the update rule (e.g., SGD, Adam) and learning rate.

4. Implement Training Loop

Outline the training loop: initialize parameters, iterate over epochs, forward pass, compute loss, backward pass, update parameters. Include mini-batch, shuffling, and convergence checks.

5. Evaluate and Validate

Describe evaluation metrics (accuracy, precision/recall, F1, AUC), validation strategy (train/val/test split, cross-validation), and how to detect overfitting/underfitting.

Key Points to Mention

Choice of loss function and its probabilistic interpretation (e.g., MLE for logistic regression).
Gradient derivation and vectorized implementation for efficiency.
Optimization algorithm (SGD, Adam) and hyperparameters (learning rate, batch size).
Regularization techniques (L1/L2, dropout) to prevent overfitting.
Evaluation metrics beyond accuracy (e.g., AUC, F1) and validation strategy.
Numerical stability (e.g., log-sum-exp, gradient clipping) and scalability (vectorization, GPU).

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