Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Interviewed for an ML engineer role at Meta. One question about data scarcity came up and it's the kind of thing that sounds straightforward until you're actually in the seat trying to organize your thoughts on the fly.

Questions Asked (1)

How do you handle data scarcity when building ML models?

Technical Trade-offsAdaptability & Ambiguity

Author's notes

I fumbled the opening a bit, jumped straight into augmentation techniques before even acknowledging why scarcity is actually a hard problem.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the type and degree of data scarcity (e.g., few labeled examples, rare classes, or no labels) and the constraints (latency, budget, privacy). Then walk through a structured decision tree of techniques—from data augmentation and transfer learning to semi-supervised and synthetic data—emphasizing validation and iteration. Close by tying your choices to business impact and how you'd measure success under uncertainty.

Pro tip: Meta values pragmatic trade-offs: always mention the cost of acquiring more data versus the cost of modeling techniques, and show you can quantify the risk of each approach. For example, 'If labeling 10k examples costs $50k but improves F1 by 5%, is that worth it?'

1. Diagnose the scarcity

Identify whether the problem is few labeled samples, class imbalance, or lack of domain data, and quantify the gap (e.g., 'We have 500 labeled examples but need 10k for a robust model').

2. Leverage existing knowledge

Apply transfer learning from pre-trained models (e.g., BERT, ResNet) and use data augmentation (e.g., SMOTE, image transforms) to expand effective dataset size.

3. Exploit unlabeled data

Use semi-supervised techniques like self-training, co-training, or contrastive learning, and consider weak supervision or active learning to label strategically.

4. Generate synthetic data

If appropriate, use generative models (e.g., GANs, VAEs) or simulation to create realistic samples, but validate that synthetic data doesn't introduce bias or distribution shift.

5. Validate and iterate

Use cross-validation, bootstrapping, or hold-out sets to estimate performance reliably, and set up a feedback loop to incorporate new data as it becomes available.

Key Points to Mention

Transfer learning and fine-tuning pre-trained models
Data augmentation techniques (e.g., SMOTE, mixup, geometric transforms)
Semi-supervised learning (e.g., pseudo-labeling, consistency regularization)
Active learning to prioritize labeling efforts
Synthetic data generation (e.g., GANs, simulation) and its risks
Evaluation strategies for small datasets (e.g., cross-validation, bootstrapping)

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