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AI Search Engineer

Kyle Park

Full-time · Job · Los_Angeles

About the role

Keyword search is dead for our use case. Our users are patent attorneys searching through 100 million patent documents for prior art — and they need semantic understanding, not keyword matching. A search for 'methods of reducing lithium dendrite formation in solid-state batteries' should find relevant patents even if they never use that exact phrase. You'll build the hybrid search system that makes this work: dense retrieval with custom embedding models, sparse retrieval for exact match fallback, re-ranking with cross-encoders, and result presentation with highlighted relevant passages. The scale is massive and the precision requirements are extreme — missing a relevant patent can cost a client millions. Our stack is Python, PyTorch for model training, LlamaIndex for retrieval orchestration, and FastAPI for serving. You'll own the search relevance pipeline end-to-end.

Responsibilities

  • Build hybrid search combining dense retrieval and sparse matching
  • Train and fine-tune embedding models for patent domain understanding
  • Design re-ranking pipelines using cross-encoder architectures
  • Implement passage highlighting and relevance explanation features
  • Own search relevance metrics and run systematic evaluation experiments

Requirements

  • 3+ years in search or information retrieval engineering
  • Experience with dense retrieval and embedding models in PyTorch
  • Familiarity with LlamaIndex or similar retrieval frameworks
  • Understanding of search relevance metrics (NDCG, MAP, MRR)
  • Strong Python skills with FastAPI and Docker

Benefits

  • Fully remote — US West Coast hours
  • Competitive salary with equity
  • $2,500 annual learning budget
  • Work on genuinely hard search problems

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

NLP & Prompt Engineering AI & Machine Learning Engineers

Skills

python pytorch rag langchain llamaindex fastapi docker

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