Machine Learning Engineer (Ranking & Retrieval)

• 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 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

Benefits

• 16” MacBook Pro
• Moving Expenses
• $200/mo commuter benefits
• Team lunches each week
• 401(k) match up to 2%
• Unlimited snacks, kombucha, and cold brew
• Health insurance
• Dental insurance
• ClickUp swag
• Teammate recognition rewards
• Flexible PTO- Experience building hybrid (lexical + vector) retrieval systems
• Strong query understanding fundamentals: intent modeling, query expansion
• Proven full ML lifecycle ownership: training, deploying, and serving models in production
• Hands-on ranker model training: feature engineering, pipelines, offline evaluation
• Experience running embedding inference at large scale
• Bachelor’s degree in Computer Science, Machine Learning, or related field
• 5+ years of ML engineering experience focused on ranking, retrieval, or information retrieval
• 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