WVU Medicine is hiring: Associate Machine Learning Engineer in morgantown

The Associate Machine Learning Engineer supports the development, testing, and deployment of machine learning models that enhance healthcare operations and patient outcomes at WVU Medicine. Working under the guidance of senior engineers and data scientists, this role applies foundational machine learning and software engineering skills to build, evaluate, and deploy ML solutions. The position contributes to CI/CD pipelines, assists with model deployment and monitoring, and supports the integration of Generative AI and large language model (LLM) capabilities into enterprise systems. It follows established engineering and MLOps practices while developing skills in building production‑grade systems.

Minimum Qualifications

• Bachelor’s degree in Machine Learning, Computer Science, Mathematics, Data Science, or related field AND one (1) year of professional experience in machine learning or software engineering (internships, academic research, and capstone projects qualify).
• Master’s degree in a related field with no prior professional experience required.

Preferred Qualifications

• Certifications or coursework related to machine learning, data science, or software engineering.
• Hands‑on experience with machine learning and LLM (large language model) frameworks.

Core Duties and Responsibilities

• Support data collection, cleaning, preprocessing, and feature engineering for ML model development.
• Assist in training, evaluating, and validating machine learning models using Python and standard ML frameworks.
• Write and maintain Python scripts and modules that are clean, documented, and production‑oriented.
• Contribute to CI/CD pipeline development and maintenance for automated model training, testing, and deployment in on‑premise Linux‑based environments.
• Build and manage Docker containers for packaging ML models and dependencies for on‑premise deployment on Linux‑based servers.
• Support productionalization of ML models, ensuring models are scalable, reliable, and ready for deployment at scale.
• Assist in the integration and testing of Generative AI and LLM‑powered features into healthcare applications and workflows.
• Document model development processes, data pipelines, and deployment steps clearly and thoroughly.
• Participate in Agile sprint ceremonies including stand‑ups, sprint planning, and retrospectives.
• Collaborate with data scientists, ML engineers, and software engineers across functional teams.
• Monitor deployed models for performance drift using available observability tools and contribute to retraining and improvement cycles.
• Stay current with advancements in ML, Generative AI, LLMs, and open‑source tools and frameworks.

Physical Requirements

• Primarily sedentary office or remote work environment.
• Prolonged periods working at a computer workstation.

Working Environment

• Normal office environment or remote work setting.

Skills And Abilities

• Proficiency in Python including data processing, model training, and API integration.
• Working knowledge of CI/CD standards for systems deployment and monitoring.
• General understanding of Docker and container orchestration.
• Understanding of Generative AI, LLMs, and prompt engineering principles.
• Good written and verbal communication skills, including ability to document technical processes clearly.
• Foundational understanding of data analysis and sensitive data management.

Additional Job Description

Scheduled Weekly Hours: 40

Shift: United States of America (Exempt)

Company: West Virginia University Health System

Address: 315 Point Marion Road, Morgantown, West Virginia

Equal Opportunity Employer

West Virginia University Health System and its subsidiaries (collectively "WVUHS") is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. WVUHS strictly prohibits and does not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, religion, creed, national origin or ancestry, ethnicity, sex (including gender, pregnancy, sexual orientation, and gender identity), age, physical or mental disability, citizenship, past, current, or prospective service in the uniformed services, genetic information, or any other characteristic protected under applicable federal, state, or local law. All WVUHS employees, other workers, and representatives are prohibited from engaging in unlawful discrimination. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.

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