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AI Coreference Resolution System Developer

Adam Li

Full-time · Job · New_York

About the role

When a document says 'Apple announced its new product line' and then three sentences later says 'the company also revealed that it would', a human reader instantly knows 'the company' and 'it' refer to Apple. For a knowledge extraction system, resolving these references correctly is the difference between building a coherent knowledge graph and building a set of disconnected facts. Our NLP platform extracts structured knowledge from unstructured text across news, legal documents, regulatory filings, and scientific literature. Coreference resolution — identifying when different mentions refer to the same entity — is a systematic weakness in our extraction pipeline. We're extracting entity attributes without connecting them reliably to the entities they describe, which limits the quality of our knowledge graph. You'll lead the coreference resolution engineering effort: building systems that resolve entity mentions, event references, and pronoun chains across document types we handle, handling the domain-specific challenges each type presents (legal documents have specific reference conventions, scientific papers have complex author-result attribution), and integrating resolution into the existing extraction pipeline to improve knowledge graph density.

Responsibilities

  • Build neural coreference resolution models handling entity mentions, pronoun chains, and event references
  • Design domain adaptation for legal, scientific, and financial document coreference conventions
  • Implement long-document coreference handling for filings and articles spanning thousands of tokens
  • Integrate resolved coreference chains into the knowledge graph entity attribute extraction pipeline
  • Create evaluation benchmarks measuring resolution accuracy per document domain and reference type
  • Monitor production resolution quality and detect errors in high-volume extraction batches

Requirements

  • 6+ years in NLP engineering with coreference resolution or information extraction experience
  • Deep understanding of neural coreference approaches and their domain adaptation challenges
  • Strong Python and PyTorch skills with HuggingFace NLP model experience
  • Experience building NLP pipelines that run reliably at production document volumes
  • Ability to evaluate and improve resolution quality using human annotation as ground truth

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Equity participation
  • 401(k) with match

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Categories

NLP & Prompt Engineering AI & Machine Learning Engineers

Skills

python pytorch hugging face spark fastapi

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Language

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Skills

python pytorch hugging face spark fastapi sql

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