Staff Machine Learning Operations Engineer
About this role
The Platform Engineering team at Garner Health is building infrastructure to redesign the U.S. healthcare system by applying proprietary clinical metrics to vast patient datasets. As the foundational dedicated MLOps Engineer, the person in this role will own the reliability, performance, and cost-efficiency of production machine learning systems that influence health outcomes for millions of patients. Responsibilities include architecting the machine learning platform, implementing automated CI/CD workflows, establishing drift monitoring, and setting the foundation for a future MLOps team.
Operating as the most senior MLOps voice in the organization, the incoming engineer will guide technical direction and collaborate closely with machine learning, data, and platform engineers. Someone who thrives in a demanding, high-accountability environment with a mission-driven focus will succeed here.
What you will do
- Assume full responsibility for the reliability, performance, functionality, and cost-efficiency of production machine learning systems by establishing SLOs, observability, and on-call rotations.
- Architect the machine learning platform, incorporating data infrastructure such as feature stores, model registries, model CI/CD, and standardized service patterns.
- Transition deployment processes from manual notebook hand-offs to automated, PR-driven CI/CD workflows complete with automated data quality checks and statistical model validation.
- Drive down cost and latency through optimized architecture, hardware choices, and model optimization.
- Lay the groundwork for a future MLOps team by establishing workflows, standards, and KPIs that facilitate rapid teammate onboarding and product health visibility.
- Design and implement automated data drift and concept drift monitoring systems to alert the team when models degrade.
What you need
- Bring seven or more years of software engineering experience with significant time spent operating machine learning or data-intensive systems in production at scale.
- Demonstrate deep experience with the modern machine learning production stack, including model serving tools like Sagemaker or Triton, feature stores, model registries, and ML CI/CD.
- Possess strong infrastructure and platform engineering fundamentals encompassing Kubernetes, containerization, cloud environments, Infrastructure as Code, observability, and incident response.
- Show hands-on experience designing machine learning platforms or significant components of them.
- Collaborate effectively across machine learning, data, platform engineering, data science, and product engineering teams while setting technical direction.
Nice to have
- Healthcare, regulated-data, or other high-stakes production ML experience
Benefits
- Equity incentive
- Flexible PTO
- Medical/Dental/Vision plan options
- 401(k)
- Teladoc Health
Skills and tech stack
Questions candidates ask
Where is this role located and what is the remote policy?
This role will be based in the New York City office located in the Financial District. Employees must be willing to work in the office 3 days per week on Tuesday, Wednesday, and Thursday.
What is the target salary range for this position?
The target salary range for this position is $298,000 to $351,000, with individual compensation depending on factors such as qualifications, skills, and applicable laws.
What technologies and tools will I be working with?
The technology stack includes Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, and Datadog.
Who does this role report to within the organization?
This role will report directly to the VP of Platform Engineering.