Openai·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

ML Engineer design interview at OpenAI focused entirely on data strategy for high-dimensional inputs. One long, open-ended question that kept branching into sub-topics. Felt more like a research discussion than a standard interview.

Questions Asked (1)

You're building an ML system with high-dimensional inputs like long text or high-resolution images. Labels require human annotators, which is expensive. How do you design your data collection and labeling strategy end to end?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This sprawled in every direction and I wasn't ready for how much ground it would cover.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (modality, budget, latency, quality bar) and then propose a phased data strategy: begin with a small, high-quality seed set to bootstrap models, then use active learning and weak supervision to scale labeling efficiently. Emphasize iterative improvement, human-in-the-loop quality control, and cost-quality trade-offs at each stage.

Pro tip: Frame your answer around reducing label complexity: use self-supervised pretraining to learn representations from unlabeled data, then fine-tune with fewer labels. Also, mention that you'd measure annotation ROI by tracking model performance per labeling dollar spent.

1. Define Requirements and Constraints

Clarify the task, input modality, expected quality, budget, and timeline. Identify what 'good enough' means for the model and how labeling errors impact downstream performance.

2. Bootstrap with a High-Quality Seed Set

Collect a small, diverse, and carefully annotated dataset to train an initial model. Use clear guidelines and multiple annotators to establish a gold standard and measure inter-annotator agreement.

3. Scale Efficiently with Active Learning and Weak Supervision

Use the seed model to identify uncertain or informative samples for labeling. Combine with weak supervision (heuristics, distant supervision, pre-trained models) to generate noisy labels, then denoise or refine them.

4. Implement Human-in-the-Loop Quality Control

Design annotation tasks with clear instructions, use consensus or adjudication for disagreements, and continuously monitor annotator quality. Incorporate model predictions to assist annotators and reduce cognitive load.

5. Iterate and Measure ROI

Evaluate model performance after each labeling round, track cost per improvement, and adjust the strategy. Consider semi-supervised learning, self-training, and synthetic data to further reduce labeling needs.

Key Points to Mention

Active learning: select samples with high uncertainty or diversity to maximize labeling efficiency.
Weak supervision: use programmatic labeling functions, distant supervision, or pre-trained models to generate noisy labels.
Semi-supervised learning: leverage unlabeled data via self-training, consistency regularization, or pretraining.
Annotation quality: guidelines, inter-annotator agreement, gold-standard questions, and adjudication.
Cost-quality trade-off: balance labeling budget with model performance; consider annotation ROI.
Human-in-the-loop: iterative model improvement with annotator feedback and model-assisted labeling.

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