Graduate Machine Learning Engineer, Cleo AI

• If you’re driven by complex challenges that push your expertise, the chance to shape something truly transformative, and the potential to share in Cleo’s success as we scale, while growing alongside a company that’s scaling fast, this might be your perfect fit
• Firstly and most importantly, all of this sounds exciting to you and you want to make a positive difference in society by improving the financial health of our users worldwide
• You’ve also read our company values which drive our ways of working and help us deliver working software to our users, learn what works and iterate quickly to improve it. You are excited to learn how to embrace these opinions and use them to deliver value
• As this is a junior position we’re looking for someone who has graduated with a Computer Science / ML / AI related degree and Masters with some commercial experience or, a relevant PhD

Desirable

• Joining a cross-functional product squad with a mix of backend engineers, data analysts, frontend engineers, user researchers, designers, UX writers and others to develop features that improve our users’ financial health
• You will be building ML models to solve customer problems across chat, payments, risk and marketing. Supported by Senior ML engineers to help you craft the problem definition, modelling approach, and deployment options
• Deploy these models into our production environments using our in-house ML platform, that you can read about on our blog. Let's have an Espresso: MLOps at Cleo
• Integrating LLMs where appropriate, following evaluation-driven development of applied AI
• Learning not only how to deploy and build models, but how to understand data, business problems and how you know what you have built works for customers
• Learning how to manage and own ML in production with our monitoring tools for; feature drift, accuracy and performance of your APIs
• Learning about our approach to data-driven development where we emit data with each feature we build so we can measure what it’s doing, and finding out how we analyse that data to detect problems and come up with new ideas
• Here are some examples, big and small, of the kinds of product feature work our ML Engineers have taken part in over the last year:
• Built ML models to understand the risk of customers using complex transactional bank data and user activity
• Built models to optimise the payment success for our customers and optimising the costs for the business. Think of this as a two-sided optimisation problem
• Building AI Agents to explore and derive insights from users transactional data
• Developed deeper understanding of users finances through models extracting meaning from transactional data. Think about your bank statement, how often do you not know what a transaction is?
• Developed contextual intent classifiers to understand what conversations users are having with Cleo and control how Cleo should respond
• Building models to understand the actions that users have available to them in Cleo and provide those contextually in conversations
• Whichever squad and part of the business you land in, you will ship changes to our millions of active users and see your work having a material impact on the financial health of those most in need