Staff Machine Learning Engineer

About the role

This is a Staff Machine Learning Operations Engineer opportunity with a fast-growing US healthtech business that partners with employers to steer members towards higher-quality doctors and lower-cost care. The role sits in Platform Engineering, reports to the VP of Platform Engineering, and is the company’s first dedicated MLOps engineer. It owns the reliability, performance and cost-efficiency of production machine learning systems and builds the platform that lets data science teams deploy models securely and consistently. You’ll architect the ML platform, including the feature store, model registry, CI/CD for models and standard service patterns. You’ll move deployments from manual notebook handoffs to pull-request-driven pipelines with automated data quality checks and statistical validation, design drift monitoring that lays the groundwork for continuous training, reduce cost and latency, and set the standards and KPIs for a future MLOps team. SLOs, observability and on-call sit with the role. The ideal candidate will have 7+ years’ software engineering experience, with significant time operating ML or data-intensive production systems at scale. Deep experience with model serving (SageMaker, Triton or similar), feature stores and model registries is expected, along with strong platform fundamentals in Kubernetes, AWS, Terraform and observability. Healthcare or other regulated-data experience would be advantageous. This is an in-office role in New York City, three days a week (Tuesday to Thursday), suited to a senior engineer who wants to build an ML platform from the ground up. Visa sponsorship is not available. We're looking for someone curious, pragmatic, and eager to work across the stack with a small senior team. You'll own end-to-end delivery of major features, influence architecture, and mentor engineers.

What you'll do

• Ship customer-facing features on a weekly cadence.
• Partner with design and product on scoping and trade-offs.
• Drive technical quality through review, testing, and docs.
• Mentor one or two engineers on your team.

Requirements