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AI README Quality and Coverage Analyzer

Hunter Carvalho

Full-time · Job · New_York

About the role

A repository README is the front door of a codebase. Developers evaluating whether to adopt a library, contribute to a project, or integrate a dependency make their first judgment call from the README. A README that's missing installation instructions, doesn't explain what the library does, has no usage examples, or lists requirements that are three major versions outdated sends most developers to a competing library. README quality is developer experience quality — and it's almost never systematically measured. We build documentation intelligence tooling used by 80,000 developers. README quality analysis is a new product feature: analyzing repository READMEs across key quality dimensions and surfacing the specific gaps that most hurt developer adoption. The dimensions that matter: does it explain what the library does? Does it have installation instructions? Does it have working code examples? Does it document the API surface? Does it explain configuration? Is it accurate relative to the current codebase? You'll build the README quality analysis system: NLP-based section detection and completeness checking, code example extraction and basic correctness validation, and API coverage analysis that cross-references README documentation against the actual public API surface of the project.

Responsibilities

  • Build README section detection classifying content sections against key quality dimensions: purpose, install, usage, API, config
  • Implement completeness scoring measuring coverage of each quality dimension across README content
  • Develop code example extraction and basic correctness validation against syntax and import checks
  • Create API coverage analysis comparing documented API surface against source code public API exports
  • Build README quality report generation surfacing the highest-impact gaps in ranked priority order
  • Develop accuracy detection flagging README sections that appear inconsistent with current codebase structure

Requirements

  • 3+ years in developer tooling, technical documentation, or NLP engineering
  • Strong Python skills with text analysis, section classification, and code parsing experience
  • Understanding of README conventions, developer experience quality, and open-source project documentation standards
  • Experience building NLP systems that analyze structured technical documents
  • Ability to design quality signals that correlate with actual developer adoption impact

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $2,500 annual professional development budget
  • 401(k) with match
  • Equity options

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Categories

AI Product & Strategy NLP & Prompt Engineering

Skills

python openai api sql fastapi docker

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

AI Product & Strategy NLP & Prompt Engineering

Skills

python openai api sql fastapi docker statistics

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