Machine Learning/AI Engineer, Associate

Acentra Health, LLC•UNAVAILABLE, UNAVAILABLE

2d•Remote

About The Position

Acentra Health is looking for a Machine Learning Engineer/AI Engineer, Associate to join our growing team. The purpose of this position is to support the design, development, and deployment of machine learning solutions that improve patient outcomes and drive innovation and efficiency in healthcare. The Associate Machine Learning Engineer works under the guidance of senior engineers and data scientists to help build data pipelines, train and evaluate models (including natural language processing and generative AI use cases), and contribute to production-ready code and documentation. Position is remote, but candidates based in Raleigh/Durham/Cary, NC or DC area preferred.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (Master’s a plus).
2+ years of relevant experience (including internships, co-ops, research, or project-based experience) building or applying machine learning solutions (preferably with proven applied AI/LLM deployments)
Working knowledge of Python (or similar) and fundamentals of machine learning and statistics.

Nice To Haves

Exposure to machine learning frameworks/libraries (e.g., scikit-learn, PyTorch, TensorFlow).
Familiarity with SQL and common data tools (e.g., Pandas, NumPy).
Basic understanding of NLP concepts; exposure to large language models (LLMs) and prompt engineering is a plus.
Experience or coursework in MLOps concepts (e.g., version control, CI/CD basics, model monitoring, experiment tracking).
Exposure to cloud platforms (Azure and/or AWS) for development or deployment is a plus.
Familiarity with healthcare data concepts/standards (e.g., HIPAA awareness, HL7/FHIR, EHR data) is desirable but not required.
Strong communication skills and willingness to learn in a multidisciplinary environment.

Responsibilities

Assist in developing and testing machine learning models and algorithms for healthcare use cases (e.g., risk stratification, prediction, decision support)
Support data preparation activities including data cleaning, feature engineering, and basic data augmentation
Help build and maintain repeatable ML workflows/pipelines for training, validation, and experiment tracking
Run model evaluations using standard metrics; document results and support troubleshooting of performance issues
Partner with software engineering/DevOps to help package, deploy, and monitor models in non-production and production environments
Create and maintain documentation, code comments, and basic runbooks; participate in code reviews and team ceremonies
Support ML pipelines including data preparation, training workflows, and deployment into applications
Stay current on advances in LLMs, GenAI, and compliance requirements (HIPAA, GDPR, FDA)