Senior Machine Learning Engineer (vnsny)

Job Description

Overview

Develops, 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.

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

What We Provide

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.
• Applies expert-level knowledge of supervised, unsupervised, and deep learning techniques to solve real-world problems using structured and unstructured data.
• Acts as a technical lead on ML engineering projects, mentoring junior engineers and contributing to long-term ML platform strategy.
• 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.
• Builds feature extraction and engineering pipelines on diverse data sets (primarily using dbt on Snowflake).
• 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

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