Senior Machine Learning Scientist - LLM & Voice

We're making Africa the first cashless continent. In 2017, over half the population in Sub-Saharan Africa had no bank account. That's for good reason—the fees are too high, the closest branch can be miles away, and nobody takes cards. Without access to financial institutions, people are forced to keep their savings under the mattress. Small business owners rely on lenders who charge extortionate rates. Parents spend hours waiting in line to pay school fees in cash. We're solving this by building financial services that just work: no account fees, instantly available, and accepted everywhere. In places where electricity, water and roads don't always work, you can still send money with Wave. In 2017, we launched a mobile app in Senegal for cash deposit, withdrawal, and peer-to-peer and business payments. Now, we have millions of users across 9 countries and are growing fast. Our goal is to make Africa the first cashless continent and that's where you come in... Wave is now the largest financial institution in Senegal and Côte d’Ivoire, with millions of users, growing rapidly year on year. And, we’re still in the early days of our product roadmap and potential impact on people’s everyday lives. We're helping millions of customers across West Africa access financial services through mobile money, and great support is fundamental to that. We believe the future of customer support lies in machines handling boring repetitive tasks so humans can focus on high value interactions that require empathy and creativity. As a Senior Machine Learning Scientist on our Support Automation product team, you'll: Bridge research and production, building AI models and agent systems that ship into real products. Architect, evaluate, and optimize autonomous voice and digital agents powering 10M+ customer interactions per month across West Africa. Proven hands-on experience building with LLMs (prompt engineering, RAG, embeddings, fine-tuning, agent orchestration etc.). Obsess over evaluation – you've built frameworks to catch hallucinations, measure groundedness, and know when to trust your system We care about the big picture. We don’t hire engineers to just ship tickets. We hire them to solve problems. That means