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AI Retrieval Ranking Engineer

Stella Tran

Full-time · Job · London

About the role

RAG systems retrieve 20 chunks and hope the right one is in there. Ours retrieves 200 candidates and re-ranks them with a learned model that understands query intent, passage relevance, and the contextual relationship between retrieved documents. The re-ranking stage is where retrieval quality jumps from adequate to excellent. You'll build and improve the re-ranking pipeline: training cross-encoder models on domain-specific relevance judgments, optimizing inference latency for real-time re-ranking of large candidate sets, and designing the evaluation framework that measures retrieval quality across different query types and difficulty levels. Retrieval ranking sits at the intersection of information retrieval and modern NLP. The techniques are well-studied but the production challenges are underappreciated — latency budgets are tight, relevance judgments are subjective, and different clients define 'relevant' differently. You'll navigate all of these.

Responsibilities

  • Train cross-encoder re-ranking models on domain-specific relevance data
  • Optimize re-ranking inference latency for real-time candidate processing
  • Design evaluation frameworks measuring retrieval quality across query types
  • Build relevance judgment collection pipelines for model training
  • Integrate re-ranking into production RAG pipelines with latency constraints

Requirements

  • 3+ years in information retrieval or NLP with ranking experience
  • Strong PyTorch and Hugging Face skills for cross-encoder training
  • Understanding of retrieval evaluation metrics (NDCG, MRR, MAP)
  • Experience with RAG architectures and vector search systems
  • Python proficiency with production API development

Benefits

  • Fully remote — UK and EU time zones
  • 25 days PTO plus bank holidays
  • Equity participation
  • $2,500 learning and conference budget

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Categories

NLP & Prompt Engineering AI & Machine Learning Engineers

Skills

python pytorch hugging face rag fastapi

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Job Type

Full-time

Level

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Language

English

Salary Range

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AI Expertise

NLP & Prompt Engineering AI & Machine Learning Engineers

Skills

python pytorch hugging face rag fastapi docker sql

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