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AI Research Methodology Extraction Engineer

Julian Johansson

Full-time · Job · New_York

About the role

Systematic reviews require researchers to extract methodology details from hundreds of papers: sample sizes, study designs, statistical methods, intervention protocols, outcome measures. A single systematic review takes a team of researchers 6-18 months of reading and data extraction. Our extraction system automates the structured data capture, reducing that to weeks. The extraction challenge is that methodology descriptions are spread across multiple paper sections, use inconsistent terminology, and often require inference from context. "We recruited 342 participants" might appear in the methods section, while "after excluding 28 for incomplete data, 314 were analyzed" appears in results. You'll build models that find, link, and reconcile these distributed methodology elements into structured records. The scientific AI team is five engineers. You'll own the methodology extraction pipeline, working between the paper processing team that handles PDF ingestion and the review platform team that presents extracted data to researchers for verification.

Responsibilities

  • Build extraction models for methodology elements: sample size, design, methods
  • Handle distributed information spanning multiple paper sections
  • Reconcile conflicting or updated numbers across methods and results sections
  • Design evaluation against researcher-annotated ground truth datasets
  • Support extraction for different research domains: clinical, social science, engineering
  • Optimize extraction for batch processing of paper collections

Requirements

  • 1+ year in NLP with information extraction or document understanding
  • Experience with scientific or biomedical text processing
  • Strong Python skills with Hugging Face and LLM frameworks
  • Understanding of research methodology and academic paper structure
  • Interest in systematic reviews and evidence synthesis

Benefits

  • Fully remote with flexible hours
  • Health and dental insurance
  • $2,500 learning and conference budget
  • Work that accelerates scientific research
  • Quarterly team meetups

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Categories

NLP & Prompt Engineering data science & analytics

Skills

python hugging face langchain sql docker

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$80k – $100k / year

AI Expertise

NLP & Prompt Engineering data science & analytics

Skills

python hugging face langchain sql docker fastapi

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