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Overview Bridges machine learning research and engineering, applying both deep ML knowledge and an ability to deliver and maintain production-grade ML services at scale. Develops, tests, and deploys machine learning models to improve clinical and business outcomes, building scalable and reliable ML services on AWS SageMaker and other platforms. Collaborates with data scientists to experiment with advanced ML/AI algorithms and new ML/AI frameworks. Ensures effective CI/CD practices, ML pipeline monitoring, and model performance management to maintain reliable ML systems. Builds and maintains upstream data pipelines, designing feature extraction and engineering pipelines to support ML training and inference. Works under general supervision. What We Provide Referral bonus opportunities Generous paid time off (PTO), starting at 30 days of paid time off and 9 company holidays Health insurance plan for you and your loved ones, including Medical, Dental, Vision, Life and Disability insurance Employer-matched retirement savings funds Personal and financial wellness programs Pre-tax flexible spending accounts (FSAs) for healthcare and dependent care Generous tuition reimbursement for qualifying degrees Opportunities for professional growth and career advancement, including internal mobility, CEU credits, and more What You Will Do Partner with data scientists, product managers, and end users to understand business priorities, frame machine learning problems, and architect solutions. Apply expert-level knowledge of supervised, unsupervised, and deep learning techniques to real-world problems using structured and unstructured data. Act as a technical lead on ML engineering projects, mentoring junior engineers and contributing to long-term ML platform strategy. Experiment with advanced model architectures using modern deep learning frameworks (e.g., PyTorch) and explore emerging AI/ML algorithms and frameworks. Build feature extraction and engineering pipelines on diverse data sets (primarily using dbt on Snowflake). Maintain and extend GitLab CI/CD pipelines for model training and deployment. Implement and maintain scalable ML pipelines and workflows using AWS SageMaker. Monitor model performance and manage model life cycles via a centralized model registry. Partner with data scientists to support model retraining and redeployment processes. Ensure data quality across all stages of the ML lifecycle. Identify gaps and evaluate tools and cloud technologies to improve ML processes. Support team members with code reviews, documentation, and best practices. Participate in special projects and perform other duties as assigned. Qualifications Licenses and Certifications: AWS certifications relevant to ML/AI, such as: AWS Certified Cloud Practitioner AWS Certified AI Practitioner AWS Certified Solutions Architect – Associate AWS Certified Machine Learning Engineer – Associate AWS Certified Data Engineer AWS Certified Machine Learning Specialty (preferred) Education: Bachelor's Degree in Computer Science or related discipline (required) Master's Degree in Computer Science or related discipline (preferred) Work Experience: Minimum of four years deploying and productionizing ML models (required) Expertise in core ML concepts (e.g., bias-variance tradeoff, feature selection, model evaluation) and experience with architectures like transformers, gradient boosting models, or time series forecasting (required) Proficiency in Python for scripting and ML development (required) Experience with data pipeline and workflow tools (e.g., Airflow) (required) Experience with ML platforms (e.g., AWS SageMaker, MLflow, Kubeflow) and understanding of model lifecycle management, CI/CD, and infrastructure-as-code (required) Proficiency in Docker and container services (required) Experience with cloud computing (AWS) and columnar databases (Snowflake) in cloud environments (required) Effective communication skills (required) Experience with version control (Git/GitLab) (required) Proficiency in bash scripting and Linux command line (required) Experience with healthcare data and deploying ML models in healthcare settings (preferred) Compensation $122,300.00 - $164,000.00 Annual About Us VNS Health

is one of the nation's largest nonprofit home and community-based health care organizations. With over 130 years of innovation, we are committed to health and well-being—helping people live, age, and heal in their own homes, connected to family and community. More than 10,000 team members serve over 43,000 neighbors daily, delivering compassionate care, expertise, and 24/7 solutions, powered by unmatched data analytics. We offer a full range of health care services, solutions, and health plans to meet diverse community needs in New York and beyond.

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