# Machine Learning Engineer, Ranking & Retrieval

**Company:** [ClickUp](https://hotfix.jobs/companies/clickup)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $200k – $250k/yr
**Experience:** 5+ years
**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
**Posted:** 2026-09-08

> 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.

## Job Description

## 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.

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