Shopify·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Shopify ML engineer interview that went deep into data labeling from scratch. The question was a beast and I don't think I fully recovered after the first few minutes.

Questions Asked (1)

You need to build an ML model but have zero labeled data. Walk through how you'd define the label, collect it ethically and at scale, and validate that the data quality is actually good.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This one sprawls in every direction and I wasn't ready for how wide it goes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business objective and how the model's predictions will be used, then propose a labeling strategy that leverages domain knowledge and existing data sources. Outline a scalable, ethical collection process with quality controls, and describe validation methods like inter-annotator agreement and downstream performance.

Pro tip: Emphasize the importance of starting with a small, high-quality labeled set to iterate quickly and measure label noise, rather than aiming for massive scale immediately. Also, mention that you'd involve domain experts early to define labeling guidelines and continuously refine them.

1. Define the Label

Translate the business problem into a precise, measurable label definition. Consider edge cases, ambiguity, and how the label will be used in production.

2. Design Ethical Collection

Choose data sources and annotation methods that respect privacy, consent, and fairness. Ensure diverse representation and avoid bias.

3. Scale Collection

Use techniques like crowdsourcing, weak supervision, or active learning to efficiently gather labels at scale while maintaining quality.

4. Validate Data Quality

Measure inter-annotator agreement, audit samples, and monitor label distribution. Use holdout sets and downstream model performance as ultimate checks.

5. Iterate and Improve

Establish a feedback loop to refine labeling guidelines, retrain models, and address quality issues as they arise.

Key Points to Mention

Label definition should align with business metrics and be actionable.
Ethical considerations: informed consent, privacy, bias mitigation, and fairness.
Scalable labeling: crowdsourcing, weak supervision, active learning, or synthetic data.
Quality validation: inter-annotator agreement (e.g., Cohen's kappa), gold standard questions, and auditing.
Iterative process: start small, measure, and scale with quality controls.
Downstream evaluation: use model performance on a held-out test set as a proxy for label quality.

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