Machine Learning Operations Engineer

What you’ll be part of

Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster.

The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.

About the role:

We are seeking an exceptional Staff Machine Learning Operations Engineer to join our Platform Engineering team. This role will report to the VP of Platform Engineering. As Garner's foundational dedicated MLOps Engineer, you will assume responsibility for the reliability, performance, and cost-efficiency of our production machine learning systems. You will lead the development of a robust platform designed to facilitate the secure and consistent deployment of models by our machine learning and data science teams. Given that these models directly influence health outcomes and cost-effectiveness for millions of patients, maintaining the highest standards of production quality is imperative.

Where you will work:

This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Own the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, including establishing SLOs, observability, and on-call responsibilities.
• Architect Garner's ML platform including required data infrastructure (including feature store, model registry and CI/CD for models), and standardized service patterns.
• Implement ML-specific CI/CD pipelines: Transition our deployment process from manual notebook hand-offs to automated, PR-driven CI/CD workflows that include automated data quality checks and statistical model validation prior to deployment.
• Drive down cost and latency through improved architecture, hardware choices, and model optimization as appropriate.
• Lay the foundation for a future Garner MLOps team, including workflows, standards, and KPIs that enables rapid teammate onboarding and helps stakeholders and teammates quickly identify the health of the team’s products, allowing engineers to focus on areas where issues reside