Senior Machine Learning Engineer, Cleo AI

• Experience in Personalisation (Recommender systems and/or behaviour modelling)
• 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
• Advanced Degree in a quantitative discipline or equivalent commercial experience

Desirable

• Senior 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
• Ultimately, we’re looking for a brilliant Senior 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
• We understand our customers, we understand their pain, and we are passionate about helping them
• You'll be joining the Machine Learning Growth Personalisation team where you will work in a cross functional squad which is focused on using ML to personalise the Cleo product
• You will work on recommender systems and/or behavioural modelling (e.g multi-armed bandits, uplift modelling + other causal modelling)
• 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. A chance to really own the whole process end to end!
• Working cross-functionally with backend engineers, data analysts and product managers
• Taking the initiative to propose & lead technical work towards problems that were previously unknown or poorly understood. Driving change at the chapter level that affects multiple squads
• Mentoring & advising colleagues on their choices of models, architecture, and evaluation
• Here are some examples, big and small, of the kinds of product feature work our ML Engineers across Cleo have taken part in over the last year:
• Built recommender systems for in-app prompts and notifications
• Built a multi-armed bandit testing framework to enable fast and low regret multi-variate testing
• Building AI Agents to explore and derive insights from users transactional data
• 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
• Developed contextual intent classifiers to understand what conversations users are having with Cleo, enabling control of how Cleo should respond
• Building ML models to understand the actions that users have available to them in Cleo and provide those contextually in conversations
• Built ML models to understand the risk of customers using bank transaction features and user activity

• Interview with a Recruiter (30 mins)
• Interview with the Hiring Manager (30 mins)
• Python Coding Interview (45 mins)
• White-boarding session (60 mins)
• Technical Discussion (45 mins)