Remote Healthcare NLP Engineer for Clinical Info Extraction

Neurex AI; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work option

Job summary

Neurex AI in the United States is seeking an NLP Engineer to develop natural-language processing systems that extract structured meaning from clinical notes and medical records. You will apply modern language models to healthcare text at scale, with clinical experts validating outputs.

Responsibilities include designing NLP pipelines, building medical NER systems, turning unstructured notes into structured data, fine-tuning and deploying large language models, and developing semantic search and

Qualifications

• Strong foundation in NLP and machine learning.
• Experience with transformer models and modern NLP architectures.
• Experience using Python, PyTorch or TensorFlow, and NLP libraries.
• Ability to work in ambiguous, real-world settings and communicate clearly.

Responsibilities

• Design NLP pipelines for clinical text understanding and information extraction.
• Build named-entity recognition systems for medical concepts and classification models for healthcare documents.
• Turn unstructured notes into structured information, including entities and relationships.
• Fine-tune and deploy large language models for healthcare applications.
• Develop semantic search, summarization, generation, and documentation quality tools.
• Work with clinical experts to assess output quality and improve accuracy.

Skills

NLP

Machine learning

Python

PyTorch/TensorFlow

Communication

Tools

PyTorch

TensorFlow

NLP libraries

Job description

Neurex AI in the United States is seeking an NLP Engineer to develop natural-language processing systems that extract structured meaning from clinical notes and medical records. You will apply modern language models to healthcare text at scale, with clinical experts validating outputs.

Responsibilities include designing NLP pipelines, building medical NER systems, turning unstructured notes into structured data, fine-tuning and deploying large language models, and developing semantic search and