Senior Machine Learning Engineer, Cleo AI

• 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
• Training and fine-tuning models to solve customer problems across our chatbot and the bank transaction data behind it
• 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. Or integrate with LLMs hosted by OpenAI, Anthropic, GCP, AWS and others
• Working cross-functionally with backend engineers, data analysts, UX writers, product managers, annotation teams, and others to ship features that improve our users’ financial health
• 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
• Keeping Cleo at the forefront of NLP by driving the adoption of appropriate state-of-the-art techniques and communicating the team’s successes and learnings at the company level & beyond
• Mentoring & advising colleagues on their choices of models, architecture, and evaluation, promoting best practices for how we use LLMs
• 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:
• 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, 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
• 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
• Whatever problem you tackle, and whichever team you join, your work will directly impact those most in need, helping to improve their financial health

• 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)