Principal Machine Learning Engineer, Cleo AI

• Experience in industry machine learning roles as a technical leader or principal/staff engineer
• Excellent knowledge of both Data Science (python, SQL) and production tools
• A deep understanding of probability and statistics fundamentals
• Big picture thinking to correctly diagnose problems and productionising research
• Top tier communication skills, to be able to partner with Product and Commercial Leaders
• Industry-leading contributions to your field, communicated through conferences, blogs, talks, or open-source projects

Desirable

• Ultimately, we’re looking for a brilliant Principal Machine Learning Engineer to join us on our mission to fight for the world's financial health
• You’ll be leading technical work within a team of adaptable, creative and product-focused engineers, who train & integrate cutting edge machine learning across a variety of products and deploy them into production for millions of users
• Training and fine-tuning models to help customers get more value from our chatbot and app through deeper personalisation, creating a smarter & more engaging experience
• Deploying these models into our production environments using our in-house ML platform, which you can read about on our blog: Let's have an Espresso: MLOps at Cleo
• Integrating our models with LLMs hosted by OpenAI, Anthropic, GCP, AWS
• Working cross-functionally with backend engineers, data analysts, UX writers, product managers, and others to ship features that improve our users’ financial health
• Driving the adoption of appropriate state-of-the-art techniques for recommendation, message campaign optimisation, and contextual bandits
• Communicating the team’s successes and learnings at the company level & beyond
• Developing a holistic view of personalisation and user-level features across Cleo, taking the initiative to extend existing approaches to benefit new areas of the app and conversations
• Supporting ML Engineers around problem framing, ML modelling, and evaluation
• 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:
• Designed and implemented AI agents to analyse and extract insights from users’ transactional data
• Developed models to interpret transactional data, enhancing the understanding of users’ finances. Think about your bank statement—how often do you not recognise a transaction on first review?
• Created contextual intent classifiers to understand user conversations with Cleo, enabling tailored and accurate platform responses
• Engineered ML models to identify and deliver relevant actions to users within Cleo, ensuring a seamless, context-aware conversational experience
• Built models to evaluate risk in customer interactions with bank transaction features and user activities
• Developed optimisation models to improve payment success rates for customers while minimising business costs, tackling this as a two-sided optimisation challenge