Machine Learning Engineer at Korapay

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