# Senior Machine Learning Operations Engineer

**Company:** [Garner Health](https://hotfix.jobs/companies/garner-health)
**Location:** New York City, NY
**Role:** ML Engineering
**Salary:** $256k – $285k/yr
**Experience:** 5+ years
**Skills:** Python, Kubernetes, AWS, SageMaker, Terraform, S3, Snowflake, Airflow, Datadog, CI/CD, Feature Stores, Model Registries, Model Serving
**Posted:** 2026-06-16

> Build and operate production ML systems and platform components for healthcare technology, partnering with ML and data science teams on model deployment, observability, and reliability.

## Job Description

## 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.
- Hands-on experience with the modern ML production stack: model serving (e.g., 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.
- A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback.

## Technologies we use

Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, Datadog

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