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.

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 ability to prototype rapidly and evaluate results honestly

Knowledge of applied machine learning, model tuning and model evaluation

Knowledge of the latest approaches in generative AI, including SoTA models

Familiarity with open-source LLM ecosystems and cloud infrastructure sufficient to bootstrap independently

Experience combining deep learning with formal or symbolic systems - this is the closest to what we are building and the strongest signal we can receive from a candidate

Familiarity with probabilistic graphical models or decision-theoretic frameworks

Experience in regulated or high-stakes deployment environments

Working in a scaled B2C environment

Experience deploying using any of: Databricks, Azure or MLflow

Research and building of foundational ML models, regardless of domain

A genuine interest in AI safety and the ability to reason carefully about systemic risk

Opportunity to join a fast-growing, award-winning and super ambitious company

Work with a friendly team of highly motivated individuals

Be in an environment where you are listened to and can actually have an impact

Thriving collaborative and inclusive company culture

Company bonus scheme

Company pension scheme

Home office furniture allowance

Personal Annual Learning and Development budget

Private Medical Insurance

Health Cash Plan (cashback on visits to the dentist & opticians etc)

Cycle to work scheme

Gympass subscription to a variety of gyms and wellbeing apps

Enhanced parental pay & leave

Hybrid working environment (2 days in our London office)

25 days holiday + bank holidays with additional days added with length of service.

Our office is in London, by the Oxo Tower

Senior AI Researcher

Quick Summary

Listing Details

Posting Health

3 other jobs at Moneyboxapp

Similar Senior AI Researcher jobs

Stay ahead of the market