Senior Machine Learning Engineer

Monogram Health

. 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.

Posted 5/7/2026full-timeRemote • California • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AzureCloudDockerPySparkPythonSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningmodel versioningautomated deployment pipelinesdata analysisPythonSQLDatabricksPySparkmodel monitoringdata drift detection

Soft Skills

problem-solvingteamworkcollaborationcommunicationpresentation skillsproactive approachstrategic planningrisk assessmentresource allocationdecision-making