Software Engineer - R&D

Who we are Moniepoint is one of Africa’s fastest-growing fintech companies, recognised by the Financial Times for three consecutive years. We provide an end-to-end financial ecosystem powering payments, banking, credit, and business tools for over 10 million users, processing more than $250B+ in annual transaction value. In 2024-2025, Moniepoint completed a Series C fundraise exceeding $200 million, backed by leading global investors including Visa, Google’s Africa Investment Fund, Development Partners International, LeapFrog Investments, Lightrock, and IFC. This investment underscores our scale, profitability, and long-term roadmap and solidifies Moniepoint’s position as one of Africa’s fintech unicorns. About the role We are looking for engineers who can build complete systems.As a Member of Technical Staff, you are responsible for solving problems end-to-end from understanding the domain to building and operating production systems. This includes APIs, distributed systems, data pipelines, and machine learning systems where appropriate.You are not defined by a role label. You are expected to operate wherever the problem is. Curious about what makes Moniepoint an incredible place to work? Check out posts on how we cultivate a culture of innovation, teamwork, and growth. Design and build production-grade systems that are reliable, scalable, and observable. Own systems end-to-end: problem → design → data → implementation → deployment → operations. Work across application services, distributed systems, infrastructure, data pipelines, and ML systems Debug complex production issues across multiple layers Make engineering trade-offs grounded in first principles Improve performance, latency, reliability, and cost efficiency Contribute to architecture and technical direction Write maintainable code and documentation Raise the engineering bar Machine Learning as Part of the Role Frame problems correctly: when to use ML vs deterministic systems Work with data end-to-end Train, evaluate, and iterate on models Build reproducible pipelines Deploy models and monitor performance, drift, and cost Debug system + model failures Frame problems correctly: when to use ML vs deterministic systems Work with data end-to-end Train, evaluate, and iterate on models Build reproducible pipelines Deploy models and monitor performance, drift, and cost Debug system + model failures