Machine Learning Engineer

OnMed • White Plains, NY

8d

About The Position

OnMed our purpose is simple but powerful...to improve the quality of life and sense of well-being in our communities by bringing access to healthcare to everyone, everywhere. Our path to everywhere has already begun, with our innovative OnMed CareStation™, a small but mighty, Clinic-in-a-Box, bringing #healthcareaccess anywhere with an outlet to plug it in. Poised to become a key component in America’s public health infrastructure, the CareStation is the only tech-enabled, human-led, hybrid care solution that combines the comprehensive experience, trust and outcomes of a clinic, with the rapid scalability of virtual care. At OnMed, every role, every day, is directly impacting the communities we serve. You’ll join a high-performing purpose-driven team, innovating to break down the barriers that keep people from the care they need. This is not just a job...it's a movement to bring access to healthcare where and when people need it most. It’s healthcare that shows up
You are a Data Scientist / ML Engineer who bridges the gap between rigorous statistical science and production-grade engineering. You design and validate models, run controlled experiments, and ship reliable, maintainable ML systems—working side by side with a software development team in a shared toolchain. You leverage AI-aided development practices to responsibly speed development, while applying your expertise in understanding the sensitivity of specific models and the need for clinical accuracy. You are skilled in proper validation techniques and identifying potential biases or weaknesses in underlying data, particularly as models are developed to support clinical diagnoses. You take full ownership of your work—from hypothesis through deployment. This is not a role where you hand off notebooks to an engineering team; you see your work through end-to-end.

Requirements

• Strong proficiency in Python for ML and data workflows.
• Experience with a deep learning or ML framework — PyTorch, TensorFlow, or similar.
• Demonstrated ability to design rigorous experiments and statistically validate model results (not just optimize a metric).
• Hands-on experience with Apache Spark / PySpark for large-scale data processing.
• Comfortable working in Git — branching, PRs, conflict resolution, and code review as standard practice.
• Familiarity with CI/CD concepts and participation in automated build/test/deploy pipelines.
• Ability to communicate technical findings clearly to both technical and non-technical stakeholders.
• Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field.
• 5–7 years of hands-on experience in a data science or ML engineering role.
• Experience working in a cloud-based data platform (Databricks, AWS SageMaker, Azure ML, or similar).

Nice To Haves

• Exposure to containerization (Docker) and/or orchestration basics.
• Experience ina regulated or data-sensitive industry (healthcare, finance, etc.)
• Background in feature stores, data versioning, or ML platform tooling.

Responsibilities

• Design and execute experiments with proper controls, including variable isolation, hypothesis testing, and statistical power analysis.
• Build, validate, and monitor machine learning models using sound. statistical methodology (cross-validation, confidence intervals, residual analysis, distributional checks, etc.)
• Write production-quality Python code, structured for maintainability, testability, and peer review.
• Develop and run ML workloads in distributed compute environments (Spark / PySpark), including feature engineering, large-scale data processing, and model training pipelines.
• Comfortable explaining the tradeoffs of AI-assisted development in the data science / ML/AI development space, and comfortable leveraging AI to speed model development.
• Collaborate within a standard SDLC: branching strategies, pull requests, code review, and CI/CD pipeline participation.
• Partner closely with data engineers, software engineers, and analysts — contributing to shared codebases and following team conventions.
• Document work clearly: experiment design docs, model cards, and pipeline documentation.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level