Staff AI Engineer

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 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.

What you’ll do

• Own the overall system architecture, understanding how components interact, where dependencies create risk and where production realities challenge theoretical designs.
• Make day-to-day architectural decisions, defining what gets built, how it is structured and the interface contracts between components, while partnering with the Director of AI and Decision Intelligence on major strategic decisions.
• Translate research ideas into production-ready system designs, evaluating integration approaches, engineering effort, trade-offs and delivery sequencing.
• Identify architectural risks early, ensuring short-term decisions do not create long-term constraints or unnecessary technical debt.
• Build prototypes that validate architectural assumptions, integration patterns and scalability requirements, rather than simply proving that a model or idea works in isolation.
• Define clear boundaries and interfaces between learned and non-learned system components, enabling both to evolve independently without introducing instability.
• Guide technical decision-making across the team, ensuring implementation choices remain aligned with the long-term architecture and product vision, even when delivery pressures favour short-term solutions.
• Act as a technical sounding board for complex design and systems challenges, helping the team make pragmatic decisions in areas with significant uncertainty or trade-offs.

Who you are

• You can reason about large, complex systems and understand how multiple moving parts interact, without needing to simplify away the difficult realities.
• You have strong architectural judgement shaped by real-world experience. You can clearly explain your thinking, defend your decisions with evidence and adapt your views when presented with better information.
• You naturally look for structural solutions rather than local fixes, focusing on the root cause rather than the symptom.
• You are comfortable operating with a high degree of autonomy, proactively identifying problems, making decisions and documenting your reasoning.
• You enjoy writing code and see it as a core part of the role. You use it to explore ideas, validate assumptions and stress-test designs, not just implement requirements.
• You understand that maintainability and clarity are as important as functionality. You care about building systems that are not only effective, but also understandable by the people who will operate and evolve them over time.
• You are motivated by technical ownership rather than people management. This is not a line management role. We are looking for a systems thinker, architect and builder who leads through technical judgement, execution and influence.
• You thrive in environments where ambiguity is high, trade-offs are complex and there is rarely a single correct answer.

Essential experience

• Experience operating at Staff Engineer, Principal Engineer or equivalent scope within teams building AI or ML-powered products and systems.
• A track record of owning end-to-end system architecture, from design through to production, for complex AI or ML systems operating under real-world technical, product and operational constraints.
• Strong software engineering fundamentals. You write clean, maintainable and reviewable code in Python or C#/.NET, and understand why engineering quality matters as systems scale.
• Deep understanding of the trade-offs involved in AI system design, including latency versus accuracy, trainability versus interpretability, modularity versus coupling, and engineering pragmatism versus theoretical elegance.
• Sufficient ML knowledge to engage credibly in discussions around model behaviour, evaluation approaches and system design. You do not need to be an ML researcher, but you should be able to understand research outputs and make sound architectural decisions about how they are deployed and integrated into production systems.
• Experience designing systems where reliability, explainability, observability and auditability are important engineering requirements, rather than afterthoughts.
• A history of making high-impact technical decisions in environments where requirements are ambiguous, trade-offs are complex and the correct path is rarely obvious.

Desirables

• Experience designing systems that combine learned and rule-based components. This is closely aligned to how Aurora operates and is one of the strongest indicators of success in the role.
• Familiarity with agentic system design, including multi-step reasoning, tool use, orchestration and the failure modes that emerge when LLMs are given structured tasks with real-world consequences.
• Experience building systems in regulated or high-stakes environments where decisions must be auditable, explainable and defensible.
• Familiarity with probabilistic representations of state, uncertainty quantification and confidence-aware decision-making, particularly within decision support or recommendation systems.
• Experience working with Databricks, Azure, AKS and/or MLflow.
• An interest in AI safety and a thoughtful approach to the risks, limitations and unintended consequences of automated decision-making systems.