Senior Machine Learning Engineer

VNS Health • Manhattan, NY • 2w ago

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.

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

What We Provide

Referral bonus opportunities

Generous paid time off (PTO), starting at 3 0 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.

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

CAREERS AT VNS Health

The future of care begins with you. Together, we will revolutionize health care in the home and community. When you join VNS Health, you become a part of something bigger. For generations, we've been a recognized leader and innovator in patient-centered and community-focused health care. At VNS Health, you'll have the opportunity to meaningfully impact lives. Including yours. Discover your next role at VNS Health.

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