Senior Machine Learning Engineer

OverviewBridges 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.

Compensation Range:$122,300.00 - $164,000.00 Annual

What We Provide

What You Will Do

Qualifications

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
• AWS Certified Machine Learning Specialty preferred

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
• Demonstrated expertise in core ML concepts (e.g., bias-variance tradeoff, feature selection, model
• evaluation) and experience implementing modern architectures such as transformers, gradient boosting
• models, or time series forecasting techniques. required
• Proficiency in Python for general-purpose scripting and ML development required
• Experience with data pipeline and workflow management tools (e.g. Airflow) required
• Experience with ML engineering platforms (e.g., AWS SageMaker, MLflow, Kubeflow) and strong understanding of model lifecycle management, CI/CD for ML, and infrastructure-as-code principles 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 assessment data preferred
• Experience training and deploying models using modern deep learning frameworks (e.g. pytorch) preferred