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AI Retrieval Latency Benchmarking Engineer

Stella Tran

Full-time · Job · Los_Angeles

About the role

LangChain, custom retrievers, and a growing vector index are the backbone of our RAG stack. We need someone who can benchmark retrieval latency across chunking strategies and cache layers without hand-holding. Fast-moving team, no bureaucracy. You'll own the retrieval evaluation harness end to end and ship improvements that directly cut p95 response times for every downstream agent we run in production.

Responsibilities

  • Build and maintain retrieval latency benchmarking suites across chunking strategies
  • Profile p95/p99 latency for vector search and reranking stages
  • Ship caching layers that cut redundant embedding calls
  • Partner with the agent platform team to define latency SLAs
  • Own the internal dashboard tracking retrieval performance over time

Requirements

  • 4+ years building production Python services
  • Hands-on experience with vector search and embedding pipelines
  • Comfortable profiling distributed systems under load
  • Familiarity with LangChain or a comparable orchestration framework

Benefits

  • Fully remote position
  • Competitive salary + equity
  • Flexible working hours
  • Health and dental coverage
  • Annual learning budget

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Categories

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python rag langchain hugging face kubernetes

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

NLP & Prompt Engineering MLOps & AI Infrastructure

Skills

python rag langchain hugging face kubernetes

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