Staff Machine Learning Operations Engineer

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.