Job Description
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 — and we 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. At Mercury, we are committed to crafting an exceptional banking* experience for startups. Our team is passionately focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators. * Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC. As part of this role, you will: Build and operate the real-time inference service that scores models for the risk decision engine, with low latency and high availability as first-class requirements Own model deployment infrastructure — registry and versioning, CI/CD with performance, bias, and consistency checks, shadow mode, and staged rollouts Build model observability: availability, latency, and error monitoring, plus drift detection as a retraining trigger Partner with Risk Data Science to take models from a clean development-to-production handoff through to produ
