Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Interviewed for an MLE role at OpenAI. The core problem was around data mining at scale, specifically how you'd surface novel or useful examples from a massive unlabeled dataset. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

You have a very large unlabeled dataset. How would you design a system to extract novel or high-signal examples from it, for example to find images containing specific objects of interest?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This is one of those questions where the scope is so wide you can talk yourself in circles.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: what 'novel' or 'high-signal' means for the specific use case, and whether there are any labels or constraints. Then propose a scalable pipeline that combines unsupervised methods (e.g., clustering, density estimation) with active learning or similarity search to surface candidate examples, and discuss how to iterate with human feedback.

Pro tip: Emphasize the importance of a human-in-the-loop system: no fully automated method is perfect, so design for efficient human review and continuous improvement. Also, mention that starting with a small, diverse seed set can bootstrap the process.

1. Clarify objectives and constraints

Ask questions to understand what 'novel' or 'high-signal' means, the scale of the dataset, available compute, and whether any labels or pretrained models can be used.

2. Choose representation and similarity metric

Decide on a feature representation (e.g., embeddings from a pretrained model) and a similarity metric (e.g., cosine distance) that captures semantic similarity for the objects of interest.

3. Select candidate mining strategy

Pick an approach to find novel or high-signal examples: clustering to find outliers, density estimation to find rare examples, or active learning to query uncertain samples.

4. Implement scalable pipeline

Design a distributed pipeline that can handle large-scale data: use approximate nearest neighbor search, mini-batch clustering, or streaming algorithms to keep computation feasible.

5. Incorporate human feedback and iterate

Set up a loop where humans label or rank selected examples, and use that feedback to refine the selection criteria and model over time.

Key Points to Mention

Use of pretrained models for feature extraction (e.g., CLIP, SimCLR) to get meaningful embeddings without labels.
Clustering algorithms (e.g., k-means, DBSCAN, hierarchical) to identify groups and outliers.
Density estimation or anomaly detection techniques (e.g., isolation forests, autoencoders) to find rare examples.
Active learning to select examples where the model is uncertain, maximizing information gain.
Approximate nearest neighbor search (e.g., FAISS, Annoy) for efficient similarity queries at scale.
Human-in-the-loop validation to ensure relevance and to provide labels for future iterations.

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