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

SeniorPrefer not to say

May 2026Remote

Summary

Shopify ML Engineer interview that threw a pretty gnarly taxonomy classification problem at me. The scope was wide enough that I wasn't sure if they wanted a full system design or just a modeling discussion, so I tried to cover both and probably spread myself too thin.

Questions Asked (1)

You're given a hierarchical tag system for products where tags overlap and have inconsistencies across levels. How would you design an end-to-end classifier for this taxonomy, covering label space definition, data cleaning, model architecture, loss function, thresholding, class imbalance, evaluation, latency, and monitoring?

System DesignTechnical Trade-offsData Modeling

Author's notes

This was a lot to unpack in one question.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a hierarchical multi-label classification task, emphasizing the need to reconcile inconsistencies in the taxonomy before modeling. Then walk through each component (label space, data cleaning, model, loss, thresholding, imbalance, evaluation, latency, monitoring) in a structured way, highlighting trade-offs and Shopify-specific considerations like scalability and product diversity.

Pro tip: Propose a pragmatic solution that balances accuracy and latency: use a two-stage approach where a fast model prunes the label space, followed by a more precise model for fine-grained classification. Also, mention the importance of human-in-the-loop for taxonomy refinement and active learning to handle evolving tags.

1. Define and Clean the Label Space

Analyze the taxonomy to identify overlaps and inconsistencies; propose a canonical label set by merging synonyms, resolving conflicts, and possibly flattening or restructuring the hierarchy. Clean training data by removing ambiguous or mislabeled examples, and consider using weak supervision to generate additional labels.

2. Design Model Architecture and Loss

Choose a hierarchical multi-label model (e.g., a neural network with a shared encoder and multiple output heads for each level, or a sequence-to-sequence model that generates paths). Use a loss that accounts for hierarchy, such as hierarchical cross-entropy or a combination of binary cross-entropy per label with a consistency regularizer.

3. Handle Class Imbalance and Thresholding

Address imbalance via resampling, class weighting, or focal loss. For thresholding, use per-class thresholds optimized on validation data, or employ a global threshold with calibration. Consider using a top-k approach for each level to ensure coverage.

4. Evaluate and Optimize for Latency

Use hierarchical evaluation metrics (e.g., hierarchical precision/recall, F1) and also consider business metrics like coverage and accuracy at each level. For latency, optimize the model via quantization, pruning, or distillation, and consider caching frequent predictions.

5. Monitor and Iterate

Set up monitoring for data drift, prediction distribution, and performance metrics. Implement feedback loops to capture user corrections and retrain periodically. Use A/B testing to validate improvements.

Key Points to Mention

Hierarchical multi-label classification and how to handle overlapping labels
Data cleaning techniques: resolving taxonomy inconsistencies, handling noisy labels, and using weak supervision
Model architecture choices: shared encoder with multiple heads, hierarchical attention, or seq2seq for path generation
Loss functions: hierarchical cross-entropy, binary cross-entropy with regularization, and handling imbalance with focal loss
Thresholding strategies: per-class thresholds, calibration, and top-k selection
Evaluation metrics: hierarchical precision/recall/F1, and business metrics like coverage and accuracy
Latency optimization: model compression, caching, and two-stage inference
Monitoring: drift detection, feedback loops, and continuous retraining

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