Senior Machine Learning Operations Engineer

Garner’s mission is to transform the healthcare economy, delivering high-quality and affordable care for all. S.

Benefits

using clear incentives and powerful, data-driven insights. Our approach guides employees to higher-quality, lower-cost care, creating a system that works better for everyone. Patients achieve better health outcomes, employers spend healthcare dollars more effectively, and physicians are rewarded for delivering exceptional care rather than performing more procedures.

Garner is one of the fastest-growing healthcare technology companies in the country. Our products are trusted by the most sophisticated employers and providers in the industry, and we are building a team of talented, mission-driven individuals who are motivated to make a meaningful impact on healthcare at scale.

About The Role

: We are seeking a Senior MLOps Engineer to join our Platform Engineering team . This role will report to the Platform Engineering Manager, Developer Experience . As an early member of Garner's MLOps function, you will help build and operate the production machine learning systems that power our products, partnering closely with our machine learning and data science teams to enable the secure and consistent deployment of models.

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

: Help ensure the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, contributing to SLOs, observability, and on-call

Responsibilities

Build key components of Garner's ML platform, including data infrastructure (such as a feature store, model registry, and CI/CD for models) and standardized service patterns. Implement ML-specific CI/CD pipelines: Help 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.

Contribute to the workflows, standards, and KPIs that support a growing MLOps function, helping teammates and stakeholders quickly identify the health of the team's products and focus on areas where issues reside. Help establish drift monitoring: Design and implement automated data drift and concept drift monitoring systems that alert the team when models degrade, laying the groundwork for future Continuous Training (CT) architectures. The ideal candidate has: 5+ years of software engineering experience, with meaningful time spent operating ML or data-intensive systems in production.

, Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML. Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response. Experience building ML platforms or significant components of one (not strictly consuming SaaS), with sound judgment around when to build vs.

buy. Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to lead projects and influence technical decisions. Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required.