Lead Machine Learning Engineer, Cleo AI

• Experience deploying multiple machine learning models into production
• 5+ years of experience in data science, machine learning engineering, or related roles
• Experience integrating and evaluating LLMs
• Excellent knowledge of both Data Science (Python, SQL) and production tools
• Understanding of probability and statistics fundamentals
• Strong ability to communicate findings to non-technical stakeholders
• Experience of leading projects involving multiple people including developing a short term roadmap and reporting progress
• Comfortable breaking down work incrementally
• We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds

Desirable

• Machine Learning Engineers at Cleo work on building novel solutions to real-world problems
• This really does vary but could be: creating chatbots to coach our users around their financial health, creating classifiers to better understand transaction data or even optimising transactions within our payments platform
• They train, deploy, and improve machine learning models in production, ensuring they deliver meaningful impact for our users
• We’re looking for our next Lead Machine Learning Engineer to join our Chat Evaluations team
• You’ll shape & guide technical work within a team of adaptable, creative and product-focused engineers, who deliver ML/AI features that improve the observability of chatbot quality and power the AI development cycle
• 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:
• Deployed best-in-class credit decisioning models which affect millions of customers, using open banking data rather than traditional credit scoring
• 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?
• Fed user interaction data into fine-tuned LLMs and contextual ranking models, so Cleo knows how to start and continue an engaging conversation
• Developed optimisation models to improve payment success rates for customers while minimising business costs, tackling this as a two-sided optimisation challenge
• Designed and implemented AI agents to analyse and extract insights from users’ transactional data

• Talent Screen with a Talent Partner (30 mins)
• Interview with the Hiring Manager (30 mins)
• Python coding Interview (45 mins)
• White-boarding session (60 mins)
• Technical Discussion (45 mins)