ClickUp
Machine Learning Engineer, Ranking & Retrieval
About this role
ClickUp seeks an experienced Machine Learning Engineer to own the full lifecycle of ranking and retrieval systems powering search across their AI-native workspace platform. You'll build and deploy models that surface relevant, permissions-aware results at massive scale across billions of documents.
What you'll do
- Train, deploy, and serve ranking models in production with end-to-end ownership
- Build ranker features, training pipelines, and offline evaluation frameworks
- Design hybrid retrieval systems combining lexical and vector search with HNSW indexing
- Execute embedding inference at billion-scale document volumes
- Improve query understanding through intent modeling and query expansion
- Create measurement frameworks to evaluate and continuously improve search quality
What they're looking for
- Ranking and retrieval systems (5+ years)
- Full ML lifecycle: training, deployment, production serving
- Hybrid retrieval (lexical + vector search)
- Large-scale embedding inference
- Query understanding and NLP fundamentals
- OpenSearch or Elasticsearch
- Feature engineering and offline evaluation
- Python or similar ML languages
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ClickUp
ClickUp builds a work management and productivity platform while investing in AI-powered automation and integration capabilities across its go-to-market and business systems infrastructure. The company is hiring for DevOps engineers, solutions engineers, and business systems engineers to support cloud infrastructure, customer implementations, and intelligent workflow automation.
- Website
- clickup.com
Likely interview questions
- Walk us through a ranking model you've trained from feature engineering through production deployment—what metrics did you optimize for?
- How have you balanced lexical and vector search in a hybrid retrieval system, and what trade-offs did you encounter at scale?