Senior Machine Learning Engineer

What We Provide

What You Will Do

• Designs and implements scalable and reliable machine learning (ML) systems used to develop, test, and deploy machine learning models.
• Leverages technologies and platforms to support reproducible feature engineering and optimized machine learning model deployment at scale.
• Leverages continuous integration/continuous delivery (CI/CD) best practices, including test automation and monitoring, to ensure successful deployment of machine learning models.
• Collaborates with data scientists on designing a continuous model retraining process that can integrate with automated model redeployment processes.
• Creates robust monitoring solutions to understand model performance and manage model life cycles via a centralized model registry.
• Expands and optimizes data pipeline architecture in order to support building of machine learning platforms from a wide variety of complex data sets.
• Partners with data scientists and IT data engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions.
• Identifies gaps and evaluates relevant tools and cloud computing technologies as needed to improve machine learning processes and build effective solutions.
• Works with clinical operations, business owners, and/or IT to understand how VNS Health applications commit records into database systems.
• Ensures data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, and transformation.
• Guides members of the Data Science team in code reviews, documentation and software engineering life cycle.
• Participates in special projects and performs other duties as assigned.

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
• Programming expertise in Python or R required
• Experience with designing, configuring, and using ML engineering platforms (e.g. Sagemaker, MLflow, Kubeflow) required
• Experience with building, deploying, and monitoring ML pipelines required
• Experience with building or using ML Ops framework required
• Experience in container services, Kubernetes and Docker required
• Experience with cloud computing (e.g. AWS) and columnar databases (e.g. Snowflake) in a cloud environment required
• Experience with version control, especially Git/GitHub required
• Effective oral, written and interpersonal communication skills required
• Experience with Unix administration preferred
• Experience building and deploying machine learning algorithms in a health care setting preferred
• Experience with medical claims, electronic medical records, and clinical assessment data preferred