Senior Machine Learning Engineer at Monogram Health

, , United States -

Full Time

Start Date

Immediate

Expiry Date

22 Aug, 26

Salary

0.0

Posted On

24 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, ML Ops, Python, SQL, Databricks, PySpark, MLflow, GitHub Actions, Docker, Azure ML, CI/CD, Model Monitoring, Data Engineering, Git, Test-driven Development, Azure Cloud Services

Industry

Hospitals and Health Care

Description

POSITION: SENIOR MACHINE LEARNING ENGINEER The Senior Machine Learning Engineer will be responsible for the independent development and deployment of scalable machine learning models, bridging the gap between research and production, and providing technical mentorship to Machine Learning (ML) Engineers and data teams. This work will contribute to the advancement of Monogram’s data science initiatives. These models will be developed using open-source tools and libraries and may be operationalized as batch or real-time model inference endpoints. The models will utilize a variety of data sources, including healthcare claims, operational data from care management platforms, and EHRs. This position will report to the VP, Data Science. 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. POSITION REQUIREMENTS * Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field. Master’s degree preferred * 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. BENEFITS * Comprehensive Benefits - Medical, dental, and vision insurance, employee assistance program, employer-paid and voluntary life insurance, disability insurance, plus health and flexible spending accounts * Financial & Retirement Support – Competitive compensation, 401k with employer match, and financial wellness resources * Time Off & Leave – Paid holidays, flexible vacation time/PSSL, and paid parental leave * Wellness & Growth – Work life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts About Monogram Health Monogram Health is a leading multispecialty provider of in-home, evidence-based care for the most complex of patients who have multiple chronic conditions. Monogram health takes a comprehensive and personalized approach to a person’s health, treating not only a disease, but all of the chronic conditions that are present - such as diabetes, hypertension, chronic kidney disease, heart failure, depression, COPD, and other metabolic disorders. Monogram Health employs a robust clinical team, leveraging specialists across multiple disciplines including nephrology, cardiology, endocrinology, pulmonology, behavioral health, and palliative care to diagnose and treat health issues; review and prescribe medication; provide guidance, education, and counselling on a patient’s healthcare options; as well as assist with daily needs such as access to food, eating healthy, transportation, financial assistance, and more. Monogram Health is available 24 hours a day, 7 days a week, and on holidays, to support and treat patients in their home. Monogram Health’s personalized and innovative treatment model is proven to dramatically improve patient outcomes and quality of life while reducing medical costs across the health care continuum.

Responsibilities

The role involves the independent development and deployment of scalable machine learning models to support clinical and operational objectives. Key duties include owning end-to-end ML workflows, implementing CI/CD pipelines, and providing technical mentorship to data teams.