Senior AI Researcher

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.

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.

• You will work with other ML researchers, data scientists and ML engineers to:
• Propose and prototype architectures that address our core problem set, with clear‑eyed assessments of engineering complexity and scalability tradeoffs
• Code proofs of concept to validate hypotheses and test the limits of theoretical approaches, grounded in empirical reality rather than theory alone
• Serve as the team’s research lead - setting the intellectual agenda, running literature reviews, and keeping the team calibrated to what is actually state of the art versus what is hype
• Challenge existing architectural decisions, including ones we are currently confident about
• Input information to the Director of AI and Decision Intelligence and the wider AI team for decisions on objective functions, data strategy, and content strategy to ensure long‑term coherence between research direction and overall system goals
• Collaborate with academic partners where relevant and possible, with scope to contribute to publishable work emerging from applied research

• You think in systems - you can hold the interaction between components in your head, reason about failure modes, and identify where theoretical elegance will break against production constraints
• You have an optimisation mindset, not just in the technical sense but in how you approach problems generally - you look for the lever, not the brute force solution
• You are comfortable with high autonomy and wide scope in a fast‑paced environment
• You deliver great results while managing your own pace sustainably
• You read the literature seriously, have opinions about it, are willing to defend them, and willing to update them
• You are not scared of ambiguity and thrive more when the problem is harder than when the solution space is clear

• 3+ years in an ML research or engineering role with meaningful exposure to text generation, agentic systems, or symbolic reasoning (one of is fine) - or equivalent academic experience with real applied components
• Demonstrated…