Train, deploy, and serve ranking models in production, owning the full ML lifecycle
Build ranker features, training pipelines, and offline evaluation frameworks
Design and scale hybrid retrieval combining lexical and vector search (including HNSW with disk
Run embedding inference at billions-of-documents scale
Improve query understanding through intent modeling and query expansion
Build permissions-aware retrieval that respects multi-tenant boundaries

🎯 Requirements

Bachelor's degree in Computer Science, Machine Learning, or related field
5+ years of ML engineering experience focused on ranking, retrieval, or information retrieval
Proven full ML lifecycle ownership: training, deploying, and serving models in production
Hands-on ranker model training: feature engineering, pipelines, offline evaluation
Experience building hybrid (lexical + vector) retrieval systems
Experience running embedding inference at large scale

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