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

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May 2026

Summary

LinkedIn MLE interview that went deep into NLP and data mining territory. The main question was a full system design for how LinkedIn's Skills feature actually works under the hood, and they wanted you to build it from scratch, not just wave your hands at a language model.

Questions Asked (1)

Design the full ML pipeline for LinkedIn's Skills feature as a data-mining problem: how would you collect candidate skill tokens, parse and tokenize resumes, construct training data with positive and negative examples using weak supervision, train a classifier to predict whether a token is a skill, evaluate it, and keep the skill taxonomy updated over time?

System DesignData ModelingTechnical Trade-offs

Author's notes

This one sprawled in every direction and I kept second-guessing where to start.

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AI HintsAI Generated

Suggested Approach

Structure your answer as an end-to-end ML system design, starting from data collection and preprocessing, then detailing weak supervision for label generation, model training and evaluation, and finally addressing taxonomy maintenance. Emphasize trade-offs and practical considerations at each stage, showing how you would handle scale and noise.

Pro tip: Highlight the importance of human-in-the-loop validation and active learning to continuously improve the skill classifier, especially for emerging skills. Also, discuss how you would measure business impact beyond model metrics, such as improvements in job matching or user engagement.

1. Data Collection and Preprocessing

Collect candidate skill tokens from resumes, job postings, and user profiles. Parse and tokenize resumes using NLP techniques (e.g., NER, POS tagging) to extract candidate skill phrases, handling variations and multi-word skills.

2. Weak Supervision for Label Generation

Use weak supervision sources like existing skill taxonomies, crowdsourcing, and heuristic rules to generate positive and negative examples. Combine these sources using a labeling function framework (e.g., Snorkel) to create a probabilistic training set.

3. Model Training and Evaluation

Train a classifier (e.g., logistic regression, BERT-based) to predict if a token is a skill. Evaluate using precision, recall, and F1, with a held-out test set. Consider class imbalance and use techniques like active learning to improve.

4. Taxonomy Maintenance and Updates

Implement a pipeline to detect emerging skills via trend analysis and user feedback. Periodically retrain the model and update the taxonomy, ensuring backward compatibility and versioning.

Key Points to Mention

Use of weak supervision to overcome lack of labeled data, with labeling functions and noise-aware aggregation.
Tokenization and normalization challenges for multi-word skills and abbreviations (e.g., 'ML' vs 'Machine Learning').
Choice of model: trade-offs between simple linear models and deep learning for scalability and accuracy.
Evaluation metrics and handling class imbalance; importance of precision vs recall in skill detection.
Continuous learning and taxonomy evolution: monitoring drift, incorporating user feedback, and active learning.
Integration with downstream applications (e.g., job matching, recommendations) and measuring business impact.

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