NLP Engineer

UnitedHealth Group•San Diego, CA

12d•$72,800 - $130,000•Remote

About The Position

The Natural Language Processing (NLP) Engineer is a key technical contributor in the design and development of state-of-the-art NLP and Artificial Intelligence (AI) for medical applications. The Sr NLP Engineer works closely with other NLP engineers, NLP developers, software engineers, and subject matter experts on current company technologies and forward-looking projects. The successful candidate will have a strong background in NLP and AI, with an emphasis on text, information retrieval, information extraction, and/or associated principles of knowledge representation and ontological design. The successful candidate will be comfortable contributing to research and design in addition to supporting team members in the ongoing maintenance of existing systems. Strong technical skills are required, as the successful candidate must be able to deliver solutions for use in a production environment. The primary responsibilities include the enhancement of existing company NLP technologies and extension of those systems to new applications. Emphasis is placed on the augmentation of various clinical knowledge resources (lexicons, nomenclatures, hierarchies, ontologies, and rule systems) underlying the core NLP and AI modules. Emphasis is also placed on evaluation of the performance of those components on clinical documentation at scale in production environments. Work includes the generation and interpretation of domain/solution analytics, and, in collaboration with engineering, enhancement of the underlying NLP modules and functionality. Primary responsibilities also include direct support of team members in the maintenance of company NLP systems for production environments and resolution of internal and external customer issues. Work will involve all aspects of software design and development from system requirements definition to implementation, integration and testing.

Requirements

4+ years of related professional work experience - significant academic NLP development experience will be considered for strong applicants with advanced degree
4+ years of experience in linguistic processes
3+ years of experience in algorithms and techniques within a computational domain with emphasis on text processing
3+ years of experience with natural language processing or formal linguistics: morphology, syntax and semantics
3+ years of experience with at least 2 of the following: Python, Java, C#
3+ years of experience in SQL
3+ years of experience integrating Copilot with GitHub or similar AI-assisted development tools to improve productivity and code quality
3+ years of experience with modern software development and DevOps ecosystems, such as: GitHub (version control, pull requests, code reviews), CI/CD pipelines and automated build/deployment workflows
3+ years of experience working with integrated development environments and tools such as: Eclipse, Visual Studio, Artifact repositories such as Artifactory/JFrog
3+ years of experience building, deploying, and maintaining containerized images in scalable production environments

Nice To Haves

Degree in Linguistics, Computer Science, or other Computational discipline
Exposure to LLMs & prompting
Experience with ontologies
Experience in medical coding or medical terminology
Graduate degree
Experience with modern AI development tools and applied AI engineering workflows

Responsibilities

Enhancement of existing company NLP technologies and extension of those systems to new applications.
Augmentation of various clinical knowledge resources (lexicons, nomenclatures, hierarchies, ontologies, and rule systems) underlying the core NLP and AI modules.
Evaluation of the performance of NLP components on clinical documentation at scale in production environments.
Generation and interpretation of domain/solution analytics.
Enhancement of the underlying NLP modules and functionality in collaboration with engineering.
Direct support of team members in the maintenance of company NLP systems for production environments.
Resolution of internal and external customer issues.
Involvement in all aspects of software design and development from system requirements definition to implementation, integration and testing.