Machine Learning Engineer at Korapay

Korapay is the marketplace for everything payments. We allow businesses and institutions to scale faster by providing them with a robust and powerful core payment engine that eliminates the complications associated with simple and bulk transactions. With our payment solutions, you can easily accept or send payments.Role Summary

We 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.

Requirements

Our Stack

Apache Spark and Airflow
Google Vertex AI
Python
SQL
GCS/BigQuery

What We're Looking For

3+ years as an ML engineer in a production environment
Strong Python and comfort with Spark for large-scale data processing
Experience with time-series modelling: decomposition, forecasting, anomaly detection
Solid grasp of the ML lifecycle as a unified discipline
Ability to work with batch infrastructure and design for it deliberately
High ownership mentality: you notice problems and fix them as opposed to waiting to be assigned
Ability to identify gaps in data-driven business processes and come up with solutions