AI Reading Complexity Adaptation Developer

Vordex Tech

Remote · $80k – $100k / year · Full-time · Posted 4 months ago

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About the role

A government benefits website written at a 12th-grade reading level is inaccessible to the 54% of US adults who read below a 6th-grade level. A medical consent form that uses clinical terminology without plain language alternatives leaves patients unable to make informed decisions. Reading complexity adaptation — automatically rewriting content at a target reading level while preserving accuracy — is an accessibility intervention with real-world consequence. We build accessibility AI tools used by 200 public sector and healthcare organizations. Reading complexity adaptation is our most-requested new capability. You'll build it: models that estimate the reading level of arbitrary text, rewriting systems that reduce complexity to a target Flesch-Kincaid level while preserving semantic accuracy, and validation that the adapted text doesn't introduce errors or change the meaning of critical information. The hardest part isn't simplifying vocabulary — it's preserving precision. A legal disclaimer simplified too aggressively becomes inaccurate. You'll work with accessibility specialists and plain language experts who'll define the quality standards your models need to meet.

Fully remote.

Responsibilities

Build reading level estimation model across the Flesch-Kincaid scale for arbitrary document types
Implement text simplification system targeting user-specified reading complexity levels
Develop semantic accuracy validation ensuring simplified text preserves original meaning
Handle domain-specific terminology that cannot be simplified without loss of precision
Create integration APIs enabling reading complexity adaptation in client document workflows
Build evaluation benchmarks using accessibility specialist assessment as quality ground truth

Requirements

3+ years in NLP engineering with text generation or document processing experience
Strong Python and HuggingFace skills with LLM-based text generation experience
Understanding of readability metrics, plain language principles, and accessibility standards
Experience building NLP systems requiring high semantic fidelity constraints
Ability to collaborate with domain experts on quality standards and edge case handling