Machine Learning Engineer, Ranking & Retrieval
Build and operate large-scale ranking and retrieval systems that power search relevance, including hybrid lexical/vector search, embeddings, query understanding, and permission-aware retrieval. Requires a bachelor's degree and 5+ years of ML engineering experience in ranking or information retrieval.
About the job
Responsibilities
- Own the full machine learning lifecycle for ranking and retrieval, from training through deployment and production serving.
- Build ranker features, training pipelines, and offline evaluation frameworks.
- Design and scale hybrid retrieval combining lexical and vector search, including HNSW with disk offloading.
- Run embedding inference at billions-of-documents scale.
- Improve query understanding through intent modeling and query expansion.
- Build permissions-aware retrieval for multi-tenant boundaries.
- Create measurement frameworks to evaluate and improve search quality.
- Collaborate with search infrastructure, AI, and backend teams to integrate ranking improvements.
Requirements
- Bachelor's degree in Computer Science, Machine Learning, or a related field.
- 5+ years of machine learning engineering experience focused on ranking, retrieval, or information retrieval.
- Experience owning the full machine learning lifecycle, including training, deployment, and production model serving.
- Hands-on experience with ranker model training, feature engineering, pipelines, and offline evaluation.
- Experience building hybrid lexical and vector retrieval systems.
- Experience running embedding inference at large scale.
- Strong query-understanding fundamentals, including intent modeling and query expansion.
Nice-to-haves
- Permission-aware retrieval and multi-tenancy experience.
- Experience indexing large-scale user-generated content.
- Experience with OpenSearch or Elasticsearch.
- Experience with sharding, index management, and real-time ingestion at scale.
- Background in natural language processing, semantic search, or agentic retrieval.
- TypeScript experience in backend systems.
Skills
Machine Learning, Ranking Models, Information Retrieval, Hybrid Retrieval, Vector Search, Lexical Search, Hnsw, Embedding Inference, Query Expansion, Opensearch, Elasticsearch, Sharding, TypeScript, Natural Language Processing, Semantic Search
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