Engineering Manager, Machine Learning

Apella

4d•$280,000 - $310,000•Remote

About The Position

Apella Technology Inc. is seeking an Engineering Manager, Machine Learning to lead teams at the intersection of applied machine learning, surgical healthcare operations, and data and machine learning operations infrastructure. The role leads three highly technical teams focused on machine learning engineering for computer vision and forecasting, as well as the Data Platform team. The manager ensures machine learning systems move reliably from research into production and deliver measurable real-world impact. The role directs department operations and manages, mentors, hires, and develops engineers and data engineers across the three teams. It oversees machine learning system architecture, model deployment, and data platform design and execution, while establishing production standards for scalability, observability, cost effectiveness, and maintainability. The manager works with Product, Clinical, Operations, and Research stakeholders to translate business and clinical needs into technical priorities and roadmaps. This position is remote and requires limited supervision.

Responsibilities

Lead three technical teams spanning machine learning engineering for computer vision and forecasting, and the Data Platform team.
Ensure machine learning systems move reliably from research to production and deliver measurable real-world impact.
Direct daily department operations by analyzing workflows, establishing priorities, developing standards, and setting deadlines.
Manage, mentor, hire, and develop engineers and data engineers across three teams.
Manage machine learning system architecture, model deployment, and data platform design and execution.
Ensure machine learning solutions are production-grade, scalable, observable, cost effective, and maintainable.
Drive best practices for machine learning model lifecycle management, data pipelines, and system reliability.
Collaborate with Product, Clinical, Operations, and Research stakeholders to translate business and clinical needs into technical priorities.
Define machine learning and data platform goals and roadmaps, balancing near-term wins with long-term scalability.
Lead teams working on distinct but interdependent problem spaces.