Senior Machine Learning Operations Engineer

Mercury 's use of machine learning in risk decisioning is growing fast in scope and in stakes. Models increasingly drive real-time decisions about fraud and financial crime, and the Machine Learning Platform (MLP) team exists to build a paved path from a trained model to a reliable production deployment, speeding up iteration, and ensuring granular production observability.

MLP owns the production ML lifecycle: the systems that take a model from registry through deployment, real-time inference, observability, and retraining. Our Data Science colleagues author and train the models. We build the platform that lets them register, deploy, and observe those models in production without carrying the operational burden themselves. We also serve low-latency, highly available scores to the decision engine that depends on them. The platform supports business decisioning broadly, with our first use cases focused on fraud risk outcomes.