About Moneybox
At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money, it's about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement.
Job Brief
We are building Aurora, an AI system designed to guide customers toward better financial outcomes. The core technical challenge is hard: given a customer with incomplete, uncertain information about their own financial situation and goals, how do you reliably converge on the right guidance - at scale, in a regulated environment, with decisions that must be auditable and traceable?
This breaks into several non-trivial subproblems. How do you efficiently resolve uncertainty about customer state through active information gathering, asking the right question at the right moment rather than exhausting the user? How do you translate natural language policy and regulatory constraints into formal optimisation logic that is both correct and inspectable?
How do you orchestrate learned and symbolic components such that the overall system behaves reliably, degrades gracefully, and can be reasoned about by humans? How do you do all of that without paying the engineering overhead on the expert parts of the system?
We have working hypotheses and committed architectural directions on all of these. We also change our minds quickly when presented with strong arguments or new evidence. If you think we’re wrong about something, we want to know.
We host most of our models internally. We develop using Databricks@Azure, and we deploy through Databricks, or directly on Azure Kubernetes Service (AKS).
This is the foremost research position in the ML team. You will report directly to the Director of AI and Decision Intelligence and work alongside a principal data scientist, senior ML engineer, senior data scientist, and two ML engineers.