Machine Learning Engineer, Ranking & Retrieval
Core
Own the full ML lifecycle for ranking and retrieval systems that power search relevance for millions of users in a multi-tenant AI workspace.
Role type
Senior IC machine learning engineer (ranking & retrieval)
Builds
Production ranking models, hybrid retrieval systems (lexical + vector), and large-scale embedding inference pipelines.
Domain
Enterprise productivity software / AI-native search
Deliverable
production ML models
Required skills
Full ML lifecycle ownership, ranker model training, hybrid retrieval system design, large-scale embedding inference, query intent modeling, permissions-aware retrieval
Preferred skills
Multi-tenancy experience, large-scale user-generated content indexing, OpenSearch/Elasticsearch expertise, NLP/semantic search background, TypeScript backend development
Technologies
HNSW, OpenSearch, Elasticsearch, TypeScript
Responsibilities
Train, deploy, and serve ranking models in production; Build ranker features, training pipelines, and offline evaluation frameworks; Design and scale hybrid retrieval combining lexical and vector search; Run embedding inference at billions-of-documents scale; Improve query understanding through intent modeling and query expansion; Create measurement frameworks to evaluate and improve search quality
Seniority
Senior, hands-on IC