AI/ML Associate Developer

HEALTHSTREAM INC•Nashville, TN

1d•$70,352 - $81,000•Hybrid

About The Position

HealthStream is the leader in healthcare workforce solutions. We help organizations work better by helping their people work smarter. HealthStream provides the leading learning, clinical development, credentialing, and scheduling applications delivered on healthcare’s #1 platform. We streamline everyday tasks while improving performance, engagement, and safety – fostering a workplace where people flourish, and care thrives. We are seeking an entry-level AI/ML Associate Developer to join our team. In this pivotal role, you will work alongside experienced AI/ML Engineers and Software Engineers, and will be given the opportunity to learn and grow in a supportive, cutting-edge technical environment. You will focus on transforming proof-of-concepts into production-ready systems, ensuring reliability, scalability, and performance in accordance with MLOps best practices. You will collaborate with team leaders and senior developers to understand and refine requirements, help design and estimate the effort for new AI/ML features, and develop quality, defect-free code that adheres to HealthStream coding and documentation standards.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field is required.
Basic theoretical and practical knowledge of core Machine Learning algorithms, statistical modeling, and deep learning concepts.
Familiarity with major ML frameworks (e.g., TensorFlow, PyTorch) and scientific computing libraries (e.g., NumPy, Pandas, Scikit-learn).
Working knowledge of relational databases (SQL) and principles of data preprocessing, cleaning, and feature engineering.
Basic understanding of the Machine Learning Operations (MLOps) lifecycle, CI/CD, and model deployment patterns (e.g., containerization using Docker).
Eagerness to learn through self-directed courseware and peer/senior staff mentoring.
Attention to detail and commitment to adhere to corporate policies and security standards.
Proficiency in the Python programming language (including object-oriented principles) is essential.
Excellent verbal and written communication skills for documenting code, explaining model performance, and collaborating with cross-functional teams.
Must have strong logic and analytical skills to debug models, handle multiple priorities simultaneously, and demonstrate well-developed problem-solving skills in optimization and scaling.
Must demonstrate excellent time-management, prioritization, attention to detail, and organization skills, especially regarding experiment tracking and version control (Git).

Nice To Haves

Ability to translate high-level mathematical or statistical concepts into robust, well-engineered, and scalable code.
Ability to work well in a team and independently, contributing to a positive and productive work environment.
Must have the ability to learn and adapt to new ML frameworks, research advancements, and cloud technologies (AWS, GCP, or Azure) rapidly.
Must have the ability to communicate clearly and effectively with both technical stakeholders (Data Scientists, Software Engineers) and non-technical stakeholders (Product Managers).
Must be able to consistently meet established project deadlines for developement, model training, testing, and deployment cycles.

Responsibilities

Adhering to all HealthStream security policies, procedures, and assigned training.
Perform basic troubleshooting and debugging of AI/ML systems using specialized tools.
Monitor AI/ML system performance post-deployment and identify areas of drift or degradation.
Understand and contribute to the Continuous Integration/Continuous Deployment (CI/CD) and MLOps principles relevant to AI/ML systems.
Utilize Integrated Development Environments (IDEs) for developing, implementing, and training ML algorithms, primarily using Python.
Leverage understanding of Source Control systems (e.g., Git) for managing model versions, code, and experiments.
Implement and refine standard ML models using frameworks like TensorFlow or PyTorch, ensuring adherence to engineering best practices.
Perform essential data preprocessing, cleaning, and feature engineering to prepare complex datasets for model training.
Develop a working knowledge of relational and NoSQL databases, assisting in fetching, organizing, and maintaining the data required for ML pipelines.
Clearly communicate model rationale, performance metrics, and technical implementation details with team members in both verbal and written form.
Document code, model configurations, and experiment results accurately for knowledge sharing and auditing purposes.
Utilize Integrated Development Environments (IDEs) for developing, implementing, and training ML algorithms, primarily using Python.
Leverage understanding of Source Control systems (e.g., Git) for managing model versions, code, and experiments.
Implement and refine standard ML models using frameworks like TensorFlow or PyTorch, ensuring adherence to engineering best practices.