# Staff Machine Learning Operations Engineer

**Company:** [Garner Health](https://hotfix.jobs/companies/garner-health)
**Location:** New York, NY
**Role:** ML Engineering
**Salary:** $298k – $351k/yr
**Experience:** 7+ years
**Skills:** Python, Kubernetes, AWS, Amazon Sagemaker, Terraform, Amazon S3, Snowflake, Apache Airflow, Datadog, Feature Stores, Model Serving, Model Registries, CI/CD
**Posted:** 2026-09-09

> Leads the reliability, architecture, deployment automation, and monitoring of production machine learning systems. Requires 7+ years of software engineering experience, deep MLOps platform expertise, and strong Kubernetes, cloud, infrastructure-as-code, and observability fundamentals.

## Job Description

## Responsibilities
- Own the reliability, performance, functionality, and cost-efficiency of production machine learning systems, including SLOs, observability, and on-call responsibilities.
- Architect the ML platform, including a feature store, model registry, ML CI/CD, data infrastructure, and standardized service patterns.
- Build automated, pull-request-driven ML CI/CD workflows with data quality checks and statistical model validation before deployment.
- Improve cost and latency through architecture, hardware, and model optimization.
- Establish workflows, standards, KPIs, and onboarding practices for a future MLOps team.
- Design automated data drift and concept drift monitoring, creating alerts for model degradation and preparing for continuous training architectures.
- Collaborate with ML, data, platform, data science, and product engineering teams; set technical direction for MLOps.

## Requirements
- 7+ years of software engineering experience, including substantial experience operating ML or data-intensive systems in production at scale.
- Deep experience with model serving, feature stores, model registries, and CI/CD for machine learning.
- Strong infrastructure and platform engineering fundamentals, including Kubernetes, containers, cloud infrastructure, Terraform/IaC, observability, and incident response.
- Experience designing ML platforms or significant platform components, with judgment about when to build versus buy.

## Nice-to-haves
- Experience with healthcare, regulated data, or other high-stakes production ML systems.

## Technologies
- Python
- Kubernetes
- AWS
- Amazon SageMaker
- Terraform
- Amazon S3
- Snowflake
- Apache Airflow
- Datadog

## Compensation
- Target salary range: **$298,000–$351,000** annually.
- Eligible for equity incentives and benefits including flexible PTO, medical, dental, vision, 401(k), and Teladoc Health.

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