Search Relevance Engineer
Design, tune, and evaluate search systems for maximum relevance using BM25, hybrid retrieval, learning-to-rank, and neural re-ranking with rigorous offline and online evaluation frameworks.
SupaScore
85Best for
- ▸Tuning BM25 parameters and field boosting weights for e-commerce product search
- ▸Building hybrid retrieval systems combining keyword matching with semantic embeddings
- ▸Implementing learning-to-rank models to improve search result ordering
- ▸Designing offline evaluation frameworks using NDCG and reciprocal rank metrics
- ▸Configuring Elasticsearch analyzers and multi-field mappings for multilingual search
What you'll get
- ●Detailed Elasticsearch configuration with optimized BM25 k1/b parameters, field boost weights, and analyzer chains with quantified relevance improvements
- ●Hybrid search architecture combining BM25 and vector similarity with reciprocal rank fusion weights and A/B test design
- ●Learning-to-rank model implementation with feature engineering, training data preparation, and offline evaluation results using NDCG@10
Not designed for ↓
- ×Building the underlying search infrastructure or Elasticsearch cluster setup
- ×Creating the machine learning models for embeddings (uses pre-trained models)
- ×Designing the user interface or search experience components
- ×Managing search infrastructure costs or scaling decisions
Query logs with user interactions, document corpus with metadata, and specific relevance requirements for the search domain.
Optimized search configuration with tuned parameters, evaluation metrics, and systematic relevance improvement recommendations.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 7 sources (3 books, 2 official docs, 1 paper, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Works well with
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Common Workflows
End-to-End Search Optimization
Build optimized search system, run controlled experiments to validate improvements, then implement real-time monitoring of search performance metrics
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