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AI Conversational Session Summarization Developer

Henry Eriksson

Full-time · Job · Los_Angeles

About the role

A long conversation history is both an asset and a liability. The asset: rich context about what the user has said, tried, and decided. The liability: every token costs compute, and a 50-turn conversation might contain 45 turns of context that is now irrelevant and 5 turns of information that is critical to the current task. We build conversational AI infrastructure for enterprise clients. Our session summarization layer compresses conversation history into structured summaries that preserve the decision-relevant content while dramatically reducing token count for subsequent turns. The challenge is selectivity: summaries that are too aggressive lose critical nuance; summaries that are too conservative do not solve the cost problem. You will build the summarization system: designing the relevance scoring that identifies which turns to preserve verbatim versus summarize versus discard, implementing the structured summary format that downstream models can reliably extract from, and building the evaluation framework that measures summarization quality against human-labeled ground truth.

Responsibilities

  • Design relevance scoring for conversational turn-level preservation decisions
  • Build structured summary format optimized for downstream model extraction
  • Implement evaluation framework measuring summarization quality against human labels
  • Develop context-sensitive compression that adapts to session topic and user intent
  • Build A/B testing infrastructure comparing summarized vs full context performance
  • Optimize summarization latency for real-time conversation serving

Requirements

  • 3+ years in NLP engineering with summarization, dialogue, or LLM systems experience
  • Experience with abstractive summarization and structured text generation
  • Familiarity with LangChain, Hugging Face, or similar NLP tooling
  • Strong Python and API development skills
  • Understanding of token economics and context window management in LLM applications

Benefits

  • Fully remote
  • Health, dental, and vision
  • $3,000 professional development budget
  • Equity participation
  • Quarterly team retreats

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Categories

NLP & Prompt Engineering Automation & AI Workflows

Skills

python hugging face langchain openai api fastapi

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

NLP & Prompt Engineering Automation & AI Workflows

Skills

python hugging face langchain openai api fastapi sql

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