Machine Learning Engineer, Ranking & Retrieval
About The Position
At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 ClickUp is seeking an experienced Machine Learning Engineer to join our Search team. You'll own the ML systems that power search relevance for millions of users and ground our AI in the right context. Your Impact at ClickUp You'll own the full ML lifecycle for ranking and retrieval, from training through deployment and production serving. Our search indexes large-scale, user-generated content across a multi-tenant platform where permissions-aware retrieval is critical. You'll build the systems that decide what surfaces first.
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
- Strong query understanding fundamentals: intent modeling, query expansion
Nice To Haves
- Permission-aware retrieval and multi-tenancy experience
- Indexing large-scale user-generated content (not small or static datasets)
- Hands-on experience with OpenSearch or Elasticsearch
- Sharding, index management, and real-time ingestion at scale
- Background in NLP, semantic search, or agentic retrieval
- Experience with TypeScript in backend systems
Responsibilities
- 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 offloading)
- 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
- Create measurement frameworks to evaluate and improve search quality
- Collaborate with Search Infrastructure, AI, and backend teams to integrate ranking improvements across the platform
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What This Job Offers
Job Type
Full-time
Career Level
Senior
