Machine Learning Engineer

CompanyKora is the marketplace for everything payments. We offer a robust payment API for payment collections, disbursements, and conversions for businesses anywhere in Africa.  Our vision, which is at the core of what we do every day, is to create a world void of digital financial barriers. We are committed to delivering reliable, secure, and easy-to-use digital financial solutions to every single customer with a guarantee that it is improving their lives. To achieve this mission, we need people like you.  We strongly believe in our ability to find Water in the Desert and pick the Sands in the Ocean. We value positive energy and clear communication, and are committed to building an inclusive environment for people from every background. Role SummaryWe run payments across Africa and are now positioned as a global fiat and stablecoin payment infrastructure. We offer mobile money, virtual bank accounts, and virtual cards for payins and payouts across multiple markets. Our data infrastructure is batch-first (Airflow + a cloud data warehouse) and we use Vertex AI for our MLOps lifecycle. The ML team is high-ownership: you will build models, design systems, ship them, and observe them in production. You will work on merchant-facing intelligence: forecasting, anomaly detection, segmentation, as well as automation and product-layer ML. If you want to build practical things that matter in a context that most ML engineers never get near, this is the role. What You'll Work On• Design and ship a per-merchant payment volume forecasting system: time-series decomposition, Africa-specific event calendars (salary cycles, MNO maintenance windows, public holidays), quantile regression for uncertainty bounds • Build and maintain fraud/ anomaly detection across the payment stack (residual-based and model-driven) with tiered alerting logic mapped to merchant risk profiles. • Own the dynamic merchant segmentation system end-to-end: rule-based and data-driven hybrid, percentile thresholds grounded in EDA, segment-transition features as ML inputs • Instrument and monitor deployed models: drift detection, retraining triggers, and evaluation pipelines via Vertex AI • Build automation tooling that sits alongside the core ML work: Airflow DAGs, pipeline scaffolding, and tooling to reduce operational toil • Contribute to product and strategic thinking.