Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Meta ML coding round where they handed me a text classification task and expected me to walk through the whole pipeline from data loading to deployment. More involved than I expected for a single session.

Questions Asked (1)

Fine-tune a pretrained transformer model (such as DistilBERT or BERT) using HuggingFace to classify text as harmful or not harmful. Walk through the full pipeline: loading the dataset, tokenizing, setting training arguments, evaluating with accuracy/precision/recall/F1, and how you'd deploy the model.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was basically the whole interview compressed into one prompt.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a clear, end-to-end pipeline: data loading and preprocessing, tokenization, model fine-tuning with HuggingFace Trainer, evaluation using multiple metrics, and deployment considerations. Emphasize trade-offs and production readiness, especially for a content moderation system at Meta's scale. Show awareness of class imbalance, threshold tuning, and latency constraints.

Pro tip: Don't just list steps—highlight the business context: for harmful content detection, false negatives are often costlier than false positives, so discuss how you'd adjust the decision threshold and monitor for drift. Mention that you'd start with a small model like DistilBERT for fast iteration, then scale to BERT or larger if needed.

1. Data Loading and Preprocessing

Load a labeled dataset (e.g., Jigsaw toxic comments) using HuggingFace Datasets, inspect class distribution, and handle imbalance via weighting or resampling. Split into train/validation/test sets, ensuring no leakage.

2. Tokenization and Model Setup

Use AutoTokenizer to tokenize text with padding/truncation to a fixed max length. Load a pretrained model like DistilBERT with AutoModelForSequenceClassification, setting num_labels=2.

3. Fine-Tuning with Trainer API

Define TrainingArguments (learning rate, batch size, epochs, evaluation strategy) and compute_metrics for accuracy, precision, recall, F1. Use Trainer to train and evaluate, leveraging early stopping and mixed precision for efficiency.

4. Evaluation and Threshold Tuning

Analyze metrics on validation set, plot precision-recall curve, and choose an optimal threshold based on business costs. Report final metrics on held-out test set.

5. Deployment and Monitoring

Export model to ONNX or TorchScript for optimized inference, deploy via a REST API (e.g., FastAPI) with batching and caching. Set up monitoring for data drift, latency, and false positive/negative rates.

Key Points to Mention

Handling class imbalance with weighted loss or focal loss, and its impact on precision/recall trade-off.
Using HuggingFace Trainer with compute_metrics to calculate accuracy, precision, recall, F1, and possibly AUC-ROC.
Threshold tuning: default 0.5 may not be optimal; adjust based on cost of false negatives vs false positives.
Efficiency considerations: mixed precision training, gradient accumulation, and using DistilBERT for faster iteration.
Deployment: model quantization, ONNX runtime, and serving with batching to meet low-latency requirements.
Monitoring: tracking data drift, model staleness, and feedback loops for continuous improvement.

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