Job Description
About Moneybox
Job Brief
We are building a personalisation system that helps match customers with the right financial pathway at the right moment, across relevance, guidance, advice and risk monitoring.
Aurora, our AI-powered financial guidance system, sits at the heart of this stack, but it is only one part of a much larger system.
The challenge is not simply scale. The harder problem is working with low engagement signals, a limited customer data footprint, strict regulatory boundaries and the need for every decision to be correct, auditable and defensible.
Standard recommender systems do not apply here. This is a different type of problem. The system needs to resolve uncertainty about customer state, understand the limits of its own confidence and adjust how strongly it communicates based on what it actually knows. It must do this without crossing into regulated advice, and in a way that can be inspected and explained.
The system has multiple interacting layers, including ranking, orchestration, policy translation and belief state management. The hardest architectural questions sit in the interaction between these layers. Building each layer well is achievable. Building the whole system in a way that is robust, scalable and easy to iterate on is the challenge we are hiring for.
This is a Staff-level individual contributor role, reporting directly to the Director of AI and Decision Intelligence. You will work alongside a Senior AI Researcher, Principal Data Scientist, Senior ML Engineer, Senior Data Scientist and two ML Engineers.
There is no line management expectation. This role is about technical leadership through hands-on contribution, architectural judgement and the quality of your reasoning.
About Moneybox
Job Brief
We are building a personalisation system that helps match customers with the right financial pathway at the right moment, across relevance, guidance, advice and risk monitoring.
Aurora, our AI-powered financial guidance system, sits at the heart of this stack, but it is only one part of a much larger system.
The challenge is not simply scale. The harder problem is working with low engagement signals, a limited customer data footprint, strict regulatory boundaries and the need for every decision to be correct, auditable and defensible.
Standard recommender systems do not apply here. This is a different type of problem. The system needs to resolve uncertainty about customer state, understand the limits of its own confidence and adjust how strongly it communicates based on what it actually knows. It must do this without crossing into regulated advice, and in a way that can be inspected and explained.
The system has multiple interacting layers, including ranking, orchestration, policy translation and belief state management. The hardest architectural questions sit in the interaction between these layers. Building each layer well is achievable. Building the whole system in a way that is robust, scalable and easy to iterate on is the challenge we are hiring for.
This is a Staff-level individual contributor role, reporting directly to the Director of AI and Decision Intelligence. You will work alongside a Senior AI Researcher, Principal Data Scientist, Senior ML Engineer, Senior Data Scientist and two ML Engineers.
There is no line management expectation. This role is about technical leadership through hands-on contribution, architectural judgement and the quality of your reasoning.