Our mission is to make the world more productive. To do this, we built the everything app for work - Tasks, Docs, Goals, and Chat. Backed by Lightspeed and a16z.
About the role
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. Train, deploy, and serve ranking models in production, owning the full ML lifecycle
What they're looking for
- 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
More about this role
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.
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.
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...
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