OverviewDevelops, tests, and deploys machine learning models to improve clinical and business, building scalable and reproducible workflows on AWS SageMaker and other platforms. Collaborates with data scientists to explore and test advanced ML/AI algorithms and new ML/AI frameworks. Ensures effective CI/CD practices, pipeline monitoring, and model performance management to maintain reliable ML systems. Builds and maintains upstream data pipelines, designing feature extraction and engineering pipelines that 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, Medical, Dental, Vision, Life and Disability
- Employer-matched retirement saving 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
- Internal mobility, generous tuition reimbursement, CEU credits, and advancement opportunities
What You Will Do
- Partners with data scientists, product managers, and end users to understand business priorities, frame machine learning problems, and architect machine learning solutions.
- Builds feature extraction and engineering pipelines on diverse data sets (primarily using dbt on Snowflake).
- Tests and evaluates advanced ML/AI algorithms and architectures to drive innovation.
- Experiments with advanced model architectures using modern deep learning frameworks (e.g. pytorch) and continually explores opportunities to leverage newly emerging AI/ML algorithms and frameworks.
- Maintains and extends GitLab CI/CD pipelines to ensure successful model training and deployment.
- Implements and maintains scalable machine learning pipelines and workflows using AWS SageMaker.
- Monitors model performance and manages model life cycles via a centralized model registry.
- Partners with data scientists to support model retraining and redeployment processes.
- Ensures data quality across all stages of the ML lifecycle.
- Identifies gaps and evaluates tools and cloud technologies to improve ML processes.
- Supports team members with code reviews, documentation, and software engineering best practices.
- Participates in special projects and performs other duties as assigned.
Qualifications
Education
- Bachelor's Degree in Computer Science or a related discipline required
- Master's Degree in Computer Science or a related discipline preferred
Work Experience
- Minimum of four years of experience deploying and productionizing machine learning models required
- Experience with data pipeline and workflow management tools (e.g. Airflow) required
- Proficiency using Python for both general-purpose scripting for AI/ML development required
- Experience with ML engineering platforms (e.g., AWS SageMaker, MLflow, Kubeflow) required
- Experience with building, deploying, and monitoring ML pipelines required
- Proficiency in Docker and other container services required
- Experience with cloud computing (e.g. AWS) and columnar databases (e.g. Snowflake) in a cloud environment required
- Effective oral, written and interpersonal communication skills required
- Experience with version control, especially Git/GitLab required
- Proficiency in bash scripting and working on the Linux command line required
- Experience building and deploying machine learning algorithms in a health care setting preferred
- Experience with medical claims, electronic medical records, and clinical assess AWS certifications relevant to ML/AI: 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
- Experience training and deploying models using modern deep learning frameworks (e.g. pytorch) preferred
Licenses and Certifications
- AWS certifications relevant to ML/AI: AWS Certified Cloud Practitioner, AWS Certified AI practitioner, AWS Certified Solutions Architect Associate, AWS Certified Machine Learning Engineer Associate, AWS Certified Data Engineer and AWS Certified Machine Learning Specialty 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. Innovating in health care for more than 130 years, our commitment to health and well-being is what drives us-we help people live, age and heal where they feel most comfortable, in their own homes, connected to their family and community. On any given day, more than 10,000 VNS Health team members deliver compassionate care, unparalleled expertise and 24/7 solutions and resources to the more than 43,000 "neighbors" who look to us for care. Powered and informed by data analytics that are unmatched in the home and community-health industry, VNS Health offers a full range of health care services, solutions and health plans designed to simplify the health care experience and meet the diverse and complex needs of the communities and people we serve in New York and beyond.
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VNS Health
paid time off, tuition reimbursement
Dec 24, 2024