Senior Machine Learning Operations Engineer

Sourced from sportstechjobs.com

Garner Health is seeking a Senior MLOps Engineer to join the Platform Engineering team, building and operating production ML systems that power healthcare products. This role involves ensuring reliability, performance, and cost-efficiency of ML systems, building ML platform components, implementing CI/CD pipelines, and establishing drift monitoring. The position is based in NYC with a hybrid schedule.

• 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 (feature store, model registry, CI/CD for models) and standardized service patterns.
• Implement ML-specific CI/CD pipelines, transitioning from manual notebook hand-offs to automated PR-driven workflows with data quality checks and statistical model validation.
• Drive down cost and latency through improved architecture, hardware choices, and model optimization.
• Contribute to workflows, standards, and KPIs for a growing MLOps function.
• Help establish drift monitoring: design and implement automated data drift and concept drift monitoring systems.

• 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), 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.
• Strong collaboration skills with ML, data, platform engineers, data scientists, and product engineering teams.

• Healthcare, regulated-data, or other high-stakes production ML experience is a plus.