Note: The job is a remote job and is open to candidates in USA. Monogram Health is a multispecialty provider of in-home, evidence-based care for patients with complex chronic conditions. The Senior Machine Learning Engineer will independently develop and deploy scalable machine learning models, manage end-to-end ML workflows, bridge research and production, and provide technical mentorship while supporting clinical and operational objectives.
Responsibilities
- Own and implement end-to-end ML workflows, including model versioning, testing, containerization, automated deployment pipelines (CI/CD), and post-deployment monitoring for performance and data drift
- Independently develop and deploy scalable, robust machine learning models to support Monogram’s operational and clinical objectives
- Conduct data analyses of large datasets to provide insights that aid strategic planning, risk assessments, and resource allocation
- Identify and bridge research and production gaps related to model outputs or explore alternative use cases for existing models to enhance reporting and decision-making processes
- Partner with program owners and data scientists to understand program goals, key performance indicators, and operational details to build and ensure that machine learning models are effectively aligned with program intentions
- Create and present machine learning solution proposals to program owners, aiming to secure buy-in and facilitate the implementation of data-driven solutions
- Identify, promote, and adhere to best practices in software development, data engineering, and machine learning to ensure high-quality and maintainable code
- Remain current with the latest advancements in machine learning and data science, applying new techniques and methodologies to improve model performance and reliability
- Continuously evaluate efficiency. accuracy, maintain and update deployed models, troubleshooting any issues that arise during deployment
Skills
- Bachelor's degree in Data Science, Computer Science, Statistics, or a related field
- Minimum of five (5) years of experience in ML Ops or DevOps
- Experience scaling AI products and machine learning models
- Use of Python for machine learning model development and deployment, and using SQL, Databricks & PySpark to extract and manipulate data for data engineering tasks
- Experience with ML Ops tools and practices (e.g., MLflow, GitHub Actions, Docker, model registries, Azure ML)
- Proficiency presenting technical concepts and models to business and executive stakeholders effectively
- Proficient in developing code and analyses following good software development practices
- Proficiency in packaging and deploying models in production environments, ideally using Azure cloud services
- Enhanced understanding of model monitoring, data drift detection, and model retraining strategies
- Experience with version control systems (GIT), CI/CD pipelines, and test-driven development
- Familiarity with cloud computing platforms, preferably Azure, for deploying and managing data science solutions
- Evidence of advanced problem-solving abilities and a proactive approach to identifying and addressing business challenges through data-driven solutions
- Demonstrated teamwork and collaboration skills, with the ability to work effectively in cross-functional teams
- Master's degree
Benefits
- Medical, dental, and vision insurance
- Employee assistance program
- Employer-paid and voluntary life insurance
- Disability insurance
- Health and flexible spending accounts
- 401k with employer match
- Financial wellness resources
- Paid holidays
- Flexible vacation time/PSSL
- Paid parental leave
- Work life assistance resources
- Physical wellness perks
- Mental health support
- Employee referral program
- BenefitHub for employee discounts
- Remote work arrangement
Company Overview