ML Infrastructure Engineer

Architect and implement full-stack solutions for both internal platforms and customer-facing products. Design and scale backend services that enable robust ML model deployment. Build deployment infrastructure that runs seamlessly across cloud and on-premise environments. Develop interfaces that make complex ML systems accessible and usable. Establish workflows that accelerate ML research-to-deployment cycles. Collaborate closely with Product and ML teams to iterate quickly and effectively. The chance to architect foundational systems at a true greenfield stage, direct collaboration with exceptional ML researchers and product builders, influence over critical technical decisions that will define Liquid’s trajectory, and the opportunity to shape how enterprises deploy efficient AI models at scale.